Context-aware compression with quantization of hierarchical transform matrices

ABSTRACT

Apparatus and method for context-aware compression. For example, one embodiment of an apparatus comprises: ray traversal/intersection circuitry to traverse rays through a hierarchical acceleration data structure to identify intersections between rays and primitives of a graphics scene; matrix compression circuitry/logic to compress hierarchical transformation matrices to generate compressed hierarchical transformation matrices by quantizing N-bit floating point data elements associated with child transforms of the hierarchical transformation matrices to variable-bit floating point numbers or integers comprising offsets from a parent transform of the child transform; and an instance processor to generate a plurality of instances of one or more base geometric objects in accordance with the compressed hierarchical transformation matrices.

BACKGROUND Field of the Invention

This invention relates generally to the field of graphics processors.More particularly, the invention relates to an apparatus and method forperforming more efficient ray tracing operations.

Description of the Related Art

Ray tracing is a technique in which a light transport is simulatedthrough physically-based rendering. Widely used in cinematic rendering,it was considered too resource-intensive for real-time performance untiljust a few years ago. One of the key operations in ray tracing isprocessing a visibility query for ray-scene intersections known as “raytraversal” which computes ray-scene intersections by traversing andintersecting nodes in a bounding volume hierarchy (BVH).

Denoising has become a critical feature for real-time ray tracing withsmooth, noiseless images. Rendering can be done across a distributedsystem on multiple devices, but so far the existing denoising frameworksall operate on a single instance on a single machine. If rendering isbeing done across multiple devices, they may not have all renderedpixels accessible for computing a denoised portion of the image.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the followingdrawings, in which:

FIG. 1 is a block diagram of an embodiment of a computer system with aprocessor having one or more processor cores and graphics processors;

FIG. 2 is a block diagram of one embodiment of a processor having one ormore processor cores, an integrated memory controller, and an integratedgraphics processor;

FIG. 3 is a block diagram of one embodiment of a graphics processorwhich may be a discreet graphics processing unit, or may be graphicsprocessor integrated with a plurality of processing cores;

FIG. 4 is a block diagram of an embodiment of a graphics-processingengine for a graphics processor;

FIG. 5 is a block diagram of another embodiment of a graphics processor;

FIGS. 6A-B illustrate examples of execution circuitry and logic;

FIG. 7 illustrates a graphics processor execution unit instructionformat according to an embodiment;

FIG. 8 is a block diagram of another embodiment of a graphics processorwhich includes a graphics pipeline, a media pipeline, a display engine,thread execution logic, and a render output pipeline;

FIG. 9A is a block diagram illustrating a graphics processor commandformat according to an embodiment;

FIG. 9B is a block diagram illustrating a graphics processor commandsequence according to an embodiment;

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system according to an embodiment;

FIGS. 11A-B illustrate an exemplary IP core development system that maybe used to manufacture an integrated circuit and an exemplary packageassembly;

FIG. 12 illustrates an exemplary system on a chip integrated circuitthat may be fabricated using one or more IP cores, according to anembodiment;

FIGS. 13A-B illustrates an exemplary graphics processor of a system on achip integrated circuit that may be fabricated using one or more IPcores;

FIG. 14A-B illustrate exemplary graphics processor architectures;

FIG. 15 illustrates one embodiment of an architecture for performinginitial training of a machine-learning architecture;

FIG. 16 illustrates one embodiment in which a machine-learning engine iscontinually trained and updated during runtime;

FIG. 17 illustrates another embodiment in which a machine-learningengine is continually trained and updated during runtime;

FIGS. 18A-B illustrate embodiments in which machine learning data isshared on a network; and

FIG. 19 illustrates one embodiment of a method for training amachine-learning engine;

FIG. 20 illustrates one embodiment in which nodes exchange ghost regiondata to perform distributed denoising operations;

FIG. 21 illustrates one embodiment of an architecture in which imagerendering and denoising operations are distributed across a plurality ofnodes;

FIG. 22 illustrates additional details of an architecture fordistributed rendering and denoising;

FIG. 23 illustrates a method in accordance with one embodiment of theinvention;

FIG. 24 illustrates one embodiment of a machine learning method;

FIG. 25 illustrates a plurality of interconnected general purposegraphics processors;

FIG. 26 illustrates a set of convolutional layers and fully connectedlayers for a machine learning implementation;

FIG. 27 illustrates one embodiment of a convolutional layer;

FIG. 28 illustrates an example of a set of interconnected nodes in amachine learning implementation;

FIG. 29 illustrates an embodiment of a training framework within which aneural network learns using a training dataset;

FIG. 30A illustrates examples of model parallelism and data parallelism;

FIG. 30B illustrates an example of a system on a chip (SoC);

FIG. 31 illustrates an example of a processing architecture whichincludes ray tracing cores and tensor cores;

FIG. 32 illustrates an example of a beam;

FIG. 33 illustrates an embodiment of an apparatus for performing beamtracing;

FIG. 34 illustrates an example of a beam hierarchy;

FIG. 35 illustrates a method for performing beam tracing;

FIG. 36 illustrates an example of a distributed ray tracing engine;

FIGS. 37-38 illustrate an example of compression performed in a raytracing system;

FIG. 39 illustrates a method in accordance with one embodiment of theinvention;

FIG. 40 illustrates an exemplary hybrid ray tracing apparatus;

FIG. 41 illustrates examples of stacks used for ray tracing operations;

FIG. 42 illustrates additional details for one embodiment of a hybridray tracing apparatus;

FIG. 43 illustrates an example of a bounding volume hierarchy;

FIG. 44 illustrates an example of a call stack and traversal statestorage;

FIG. 45 illustrates one embodiment for compressing transformationmatrices;

FIG. 46 illustrates one embodiment which includes procedurally generatedtransform matrices; and

FIG. 47 illustrates one example of a method for generated compressedtransform matrices.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention described below. Itwill be apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without some of thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form to avoid obscuring the underlyingprinciples of the embodiments of the invention.

Exemplary Graphics Processor Architectures and Data Types

System Overview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. In various embodiments the system 100 includes one or moreprocessors 102 and one or more graphics processors 108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 102 or processorcores 107. In one embodiment, the system 100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

In one embodiment the system 100 can include, or be incorporated withina server-based gaming platform, a game console, including a game andmedia console, a mobile gaming console, a handheld game console, or anonline game console. In some embodiments the system 100 is a mobilephone, smart phone, tablet computing device or mobile Internet device.The processing system 100 can also include, couple with, or beintegrated within a wearable device, such as a smart watch wearabledevice, smart eyewear device, augmented reality device, or virtualreality device. In some embodiments, the processing system 100 is atelevision or set top box device having one or more processors 102 and agraphical interface generated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 107 is configured to process aspecific instruction set 109. In some embodiments, instruction set 109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 107 may each process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 is additionally includedin processor 102 which may include different types of registers forstoring different types of data (e.g., integer registers, floating pointregisters, status registers, and an instruction pointer register). Someregisters may be general-purpose registers, while other registers may bespecific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with oneor more interface bus(es) 110 to transmit communication signals such asaddress, data, or control signals between processor 102 and othercomponents in the system 100. The interface bus 110, in one embodiment,can be a processor bus, such as a version of the Direct Media Interface(DMI) bus. However, processor busses are not limited to the DMI bus, andmay include one or more Peripheral Component Interconnect buses (e.g.,PCI, PCI Express), memory busses, or other types of interface busses. Inone embodiment the processor(s) 102 include an integrated memorycontroller 116 and a platform controller hub 130. The memory controller116 facilitates communication between a memory device and othercomponents of the system 100, while the platform controller hub (PCH)130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random access memory (DRAM)device, a static random access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment thememory device 120 can operate as system memory for the system 100, tostore data 122 and instructions 121 for use when the one or moreprocessors 102 executes an application or process. Memory controller 116also couples with an optional external graphics processor 112, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations. In some embodiments adisplay device 111 can connect to the processor(s) 102. The displaydevice 111 can be one or more of an internal display device, as in amobile electronic device or a laptop device or an external displaydevice attached via a display interface (e.g., DisplayPort, etc.). Inone embodiment the display device 111 can be a head mounted display(HMD) such as a stereoscopic display device for use in virtual reality(VR) applications or augmented reality (AR) applications.

In some embodiments the platform controller hub 130 enables peripheralsto connect to memory device 120 and processor 102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 146, a network controller 134, a firmware interface 128, awireless transceiver 126, touch sensors 125, a data storage device 124(e.g., hard disk drive, flash memory, etc.). The data storage device 124can connect via a storage interface (e.g., SATA) or via a peripheralbus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). The touch sensors 125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver.The firmware interface 128 enables communication with system firmware,and can be, for example, a unified extensible firmware interface (UEFI).The network controller 134 can enable a network connection to a wirednetwork. In some embodiments, a high-performance network controller (notshown) couples with the interface bus 110. The audio controller 146, inone embodiment, is a multi-channel high definition audio controller. Inone embodiment the system 100 includes an optional legacy I/O controller140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to thesystem. The platform controller hub 130 can also connect to one or moreUniversal Serial Bus (USB) controllers 142 connect input devices, suchas keyboard and mouse 143 combinations, a camera 144, or other USB inputdevices.

It will be appreciated that the system 100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 116 and platform controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 112. In one embodiment the platform controller hub 130 and/ormemory controller 1160 may be external to the one or more processor(s)102. For example, the system 100 can include an external memorycontroller 116 and platform controller hub 130, which may be configuredas a memory controller hub and peripheral controller hub within a systemchipset that is in communication with the processor(s) 102.

FIG. 2 is a block diagram of an embodiment of a processor 200 having oneor more processor cores 202A-202N, an integrated memory controller 214,and an integrated graphics processor 208. Those elements of FIG. 2having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor200 can include additional cores up to and including additional core202N represented by the dashed lined boxes. Each of processor cores202A-202N includes one or more internal cache units 204A-204N. In someembodiments each processor core also has access to one or more sharedcached units 206.

The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more PCI or PCI express busses. System agent core 210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 210 includes one or more integratedmemory controllers 214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, the systemagent core 210 also includes a display controller 211 to drive graphicsprocessor output to one or more coupled displays. In some embodiments,display controller 211 may also be a separate module coupled with thegraphics processor via at least one interconnect, or may be integratedwithin the graphics processor 208.

In some embodiments, a ring based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 3 is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 314 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 320.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 320 can be an internal orexternal display device. In one embodiment the display device 320 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 300 includes a video codec engine 306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, as well as the Society of Motion Picture &Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic ExpertsGroup (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3 are illustrated. The media pipeline 316is optional in some embodiments of the GPE 410 and may not be explicitlyincluded within the GPE 410. For example and in at least one embodiment,a separate media and/or image processor is coupled to the GPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414. In one embodiment thegraphics core array 414 include one or more blocks of graphics cores(e.g., graphics core(s) 415A, graphics core(s) 415B), each blockincluding one or more graphics cores. Each graphics core includes a setof graphics execution resources that includes general-purpose andgraphics specific execution logic to perform graphics and computeoperations, as well as fixed function texture processing and/or machinelearning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 includes fixed function andprogrammable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 414. The graphics core array 414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 415A-414B of the graphic core array 414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 414 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallelgeneral-purpose computational operations, in addition to graphicsprocessing operations. The general-purpose logic can perform processingoperations in parallel or in conjunction with general-purpose logicwithin the processor core(s) 107 of FIG. 1 or core 202A-202N as in FIG.2.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments the URB418 may be used to send data between different threads executing on thegraphics core array 414. In some embodiments the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420.

A shared function is implemented where the demand for a givenspecialized function is insufficient for inclusion within the graphicscore array 414. Instead a single instantiation of that specializedfunction is implemented as a stand-alone entity in the shared functionlogic 420 and shared among the execution resources within the graphicscore array 414. The precise set of functions that are shared between thegraphics core array 414 and included within the graphics core array 414varies across embodiments. In some embodiments, specific sharedfunctions within the shared function logic 420 that are used extensivelyby the graphics core array 414 may be included within shared functionlogic 416 within the graphics core array 414. In various embodiments,the shared function logic 416 within the graphics core array 414 caninclude some or all logic within the shared function logic 420. In oneembodiment, all logic elements within the shared function logic 420 maybe duplicated within the shared function logic 416 of the graphics corearray 414. In one embodiment the shared function logic 420 is excludedin favor of the shared function logic 416 within the graphics core array414.

FIG. 5 is a block diagram of hardware logic of a graphics processor core500, according to some embodiments described herein. Elements of FIG. 5having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Theillustrated graphics processor core 500, in some embodiments, isincluded within the graphics core array 414 of FIG. 4. The graphicsprocessor core 500, sometimes referred to as a core slice, can be one ormultiple graphics cores within a modular graphics processor. Thegraphics processor core 500 is exemplary of one graphics core slice, anda graphics processor as described herein may include multiple graphicscore slices based on target power and performance envelopes. Eachgraphics processor core 500 can include a fixed function block 530coupled with multiple sub-cores 501A-501F, also referred to assub-slices, that include modular blocks of general-purpose and fixedfunction logic.

In some embodiments the fixed function block 530 includes ageometry/fixed function pipeline 536 that can be shared by all sub-coresin the graphics processor core 500, for example, in lower performanceand/or lower power graphics processor implementations. In variousembodiments, the geometry/fixed function pipeline 536 includes a 3Dfixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4)a video front-end unit, a thread spawner and thread dispatcher, and aunified return buffer manager, which manages unified return buffers,such as the unified return buffer 418 of FIG. 4.

In one embodiment the fixed function block 530 also includes a graphicsSoC interface 537, a graphics microcontroller 538, and a media pipeline539. The graphics SoC interface 537 provides an interface between thegraphics processor core 500 and other processor cores within a system ona chip integrated circuit. The graphics microcontroller 538 is aprogrammable sub-processor that is configurable to manage variousfunctions of the graphics processor core 500, including thread dispatch,scheduling, and pre-emption. The media pipeline 539 (e.g., mediapipeline 316 of FIG. 3 and FIG. 4) includes logic to facilitate thedecoding, encoding, pre-processing, and/or post-processing of multimediadata, including image and video data. The media pipeline 539 implementmedia operations via requests to compute or sampling logic within thesub-cores 501-501F.

In one embodiment the SoC interface 537 enables the graphics processorcore 500 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, the systemRAM, and/or embedded on-chip or on-package DRAM. The SoC interface 537can also enable communication with fixed function devices within theSoC, such as camera imaging pipelines, and enables the use of and/orimplements global memory atomics that may be shared between the graphicsprocessor core 500 and CPUs within the SoC. The SoC interface 537 canalso implement power management controls for the graphics processor core500 and enable an interface between a clock domain of the graphic core500 and other clock domains within the SoC. In one embodiment the SoCinterface 537 enables receipt of command buffers from a command streamerand global thread dispatcher that are configured to provide commands andinstructions to each of one or more graphics cores within a graphicsprocessor. The commands and instructions can be dispatched to the mediapipeline 539, when media operations are to be performed, or a geometryand fixed function pipeline (e.g., geometry and fixed function pipeline536, geometry and fixed function pipeline 514) when graphics processingoperations are to be performed.

The graphics microcontroller 538 can be configured to perform variousscheduling and management tasks for the graphics processor core 500. Inone embodiment the graphics microcontroller 538 can perform graphicsand/or compute workload scheduling on the various graphics parallelengines within execution unit (EU) arrays 502A-502F, 504A-504F withinthe sub-cores 501A-501F. In this scheduling model, host softwareexecuting on a CPU core of an SoC including the graphics processor core500 can submit workloads one of multiple graphic processor doorbells,which invokes a scheduling operation on the appropriate graphics engine.Scheduling operations include determining which workload to run next,submitting a workload to a command streamer, pre-empting existingworkloads running on an engine, monitoring progress of a workload, andnotifying host software when a workload is complete. In one embodimentthe graphics microcontroller 538 can also facilitate low-power or idlestates for the graphics processor core 500, providing the graphicsprocessor core 500 with the ability to save and restore registers withinthe graphics processor core 500 across low-power state transitionsindependently from the operating system and/or graphics driver softwareon the system.

The graphics processor core 500 may have greater than or fewer than theillustrated sub-cores 501A-501F, up to N modular sub-cores. For each setof N sub-cores, the graphics processor core 500 can also include sharedfunction logic 510, shared and/or cache memory 512, a geometry/fixedfunction pipeline 514, as well as additional fixed function logic 516 toaccelerate various graphics and compute processing operations. Theshared function logic 510 can include logic units associated with theshared function logic 420 of FIG. 4 (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin the graphics processor core 500. The shared and/or cache memory512 can be a last-level cache for the set of N sub-cores 501A-501Fwithin the graphics processor core 500, and can also serve as sharedmemory that is accessible by multiple sub-cores. The geometry/fixedfunction pipeline 514 can be included instead of the geometry/fixedfunction pipeline 536 within the fixed function block 530 and caninclude the same or similar logic units.

In one embodiment the graphics processor core 500 includes additionalfixed function logic 516 that can include various fixed functionacceleration logic for use by the graphics processor core 500. In oneembodiment the additional fixed function logic 516 includes anadditional geometry pipeline for use in position only shading. Inposition-only shading, two geometry pipelines exist, the full geometrypipeline within the geometry/fixed function pipeline 516, 536, and acull pipeline, which is an additional geometry pipeline which may beincluded within the additional fixed function logic 516. In oneembodiment the cull pipeline is a trimmed down version of the fullgeometry pipeline. The full pipeline and the cull pipeline can executedifferent instances of the same application, each instance having aseparate context. Position only shading can hide long cull runs ofdiscarded triangles, enabling shading to be completed earlier in someinstances. For example and in one embodiment the cull pipeline logicwithin the additional fixed function logic 516 can execute positionshaders in parallel with the main application and generally generatescritical results faster than the full pipeline, as the cull pipelinefetches and shades only the position attribute of the vertices, withoutperforming rasterization and rendering of the pixels to the framebuffer. The cull pipeline can use the generated critical results tocompute visibility information for all the triangles without regard towhether those triangles are culled. The full pipeline (which in thisinstance may be referred to as a replay pipeline) can consume thevisibility information to skip the culled triangles to shade only thevisible triangles that are finally passed to the rasterization phase.

In one embodiment the additional fixed function logic 516 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 501A-501F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 501A-501F include multiple EUarrays 502A-502F, 504A-504F, thread dispatch and inter-threadcommunication (TD/IC) logic 503A-503F, a 3D (e.g., texture) sampler505A-505F, a media sampler 506A-506F, a shader processor 507A-507F, andshared local memory (SLM) 508A-508F. The EU arrays 502A-502F, 504A-504Feach include multiple execution units, which are general-purposegraphics processing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. The TD/IC logic 503A-503F performs local thread dispatch andthread control operations for the execution units within a sub-core andfacilitate communication between threads executing on the executionunits of the sub-core. The 3D sampler 505A-505F can read texture orother 3D graphics related data into memory. The 3D sampler can readtexture data differently based on a configured sample state and thetexture format associated with a given texture. The media sampler506A-506F can perform similar read operations based on the type andformat associated with media data. In one embodiment, each graphicssub-core 501A-501F can alternately include a unified 3D and mediasampler. Threads executing on the execution units within each of thesub-cores 501A-501F can make use of shared local memory 508A-508F withineach sub-core, to enable threads executing within a thread group toexecute using a common pool of on-chip memory.

Execution Units

FIGS. 6A-6B illustrate thread execution logic 600 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein. Elements of FIGS. 6A-6B having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 6A illustrates anoverview of thread execution logic 600, which can include a variant ofthe hardware logic illustrated with each sub-core 501A-501F of FIG. 5.FIG. 6B illustrates exemplary internal details of an execution unit.

As illustrated in FIG. 6A, in some embodiments thread execution logic600 includes a shader processor 602, a thread dispatcher 604,instruction cache 606, a scalable execution unit array including aplurality of execution units 608A-608N, a sampler 610, a data cache 612,and a data port 614. In one embodiment the scalable execution unit arraycan dynamically scale by enabling or disabling one or more executionunits (e.g., any of execution unit 608A, 608B, 608C, 608D, through608N-1 and 608N) based on the computational requirements of a workload.In one embodiment the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 600 includes one or more connectionsto memory, such as system memory or cache memory, through one or more ofinstruction cache 606, data port 614, sampler 610, and execution units608A-608N. In some embodiments, each execution unit (e.g. 608A) is astand-alone programmable general-purpose computational unit that iscapable of executing multiple simultaneous hardware threads whileprocessing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 608A-608N is scalableto include any number individual execution units.

In some embodiments, the execution units 608A-608N are primarily used toexecute shader programs. A shader processor 602 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 604. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 608A-608N.For example, a geometry pipeline can dispatch vertex, tessellation, orgeometry shaders to the thread execution logic for processing. In someembodiments, thread dispatcher 604 can also process runtime threadspawning requests from the executing shader programs.

In some embodiments, the execution units 608A-608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 608A-608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units608A-608N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader.

Each execution unit in execution units 608A-608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 609A-609N having thread control logic (607A-607N)that is common to the fused EUs. Multiple EUs can be fused into an EUgroup. Each EU in the fused EU group can be configured to execute aseparate SIMD hardware thread. The number of EUs in a fused EU group canvary according to embodiments. Additionally, various SIMD widths can beperformed per-EU, including but not limited to SIMD8, SIMD16, andSIMD32. Each fused graphics execution unit 609A-609N includes at leasttwo execution units. For example, fused execution unit 609A includes afirst EU 608A, second EU 608B, and thread control logic 607A that iscommon to the first EU 608A and the second EU 608B. The thread controllogic 607A controls threads executed on the fused graphics executionunit 609A, allowing each EU within the fused execution units 609A-609Nto execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 606) are included in thethread execution logic 600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,612) are included to cache thread data during thread execution. In someembodiments, a sampler 610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 602dispatches threads to an execution unit (e.g., 608A) via threaddispatcher 604. In some embodiments, shader processor 602 uses texturesampling logic in the sampler 610 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 614 provides a memory accessmechanism for the thread execution logic 600 to output processed data tomemory for further processing on a graphics processor output pipeline.In some embodiments, the data port 614 includes or couples to one ormore cache memories (e.g., data cache 612) to cache data for memoryaccess via the data port.

As illustrated in FIG. 6B, a graphics execution unit 608 can include aninstruction fetch unit 637, a general register file array (GRF) 624, anarchitectural register file array (ARF) 626, a thread arbiter 622, asend unit 630, a branch unit 632, a set of SIMD floating point units(FPUs) 634, and in one embodiment a set of dedicated integer SIMD ALUs635. The GRF 624 and ARF 626 includes the set of general register filesand architecture register files associated with each simultaneoushardware thread that may be active in the graphics execution unit 608.In one embodiment, per thread architectural state is maintained in theARF 626, while data used during thread execution is stored in the GRF624. The execution state of each thread, including the instructionpointers for each thread, can be held in thread-specific registers inthe ARF 626.

In one embodiment the graphics execution unit 608 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads.

In one embodiment, the graphics execution unit 608 can co-issue multipleinstructions, which may each be different instructions. The threadarbiter 622 of the graphics execution unit thread 608 can dispatch theinstructions to one of the send unit 630, branch unit 6342, or SIMDFPU(s) 634 for execution. Each execution thread can access 128general-purpose registers within the GRF 624, where each register canstore 32 bytes, accessible as a SIMD 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 624, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment up to seven threads can executesimultaneously, although the number of threads per execution unit canalso vary according to embodiments. In an embodiment in which seventhreads may access 4 Kbytes, the GRF 624 can store a total of 28 Kbytes.Flexible addressing modes can permit registers to be addressed togetherto build effectively wider registers or to represent strided rectangularblock data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 630. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 632 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 608 includes one or moreSIMD floating point units (FPU(s)) 634 to perform floating-pointoperations. In one embodiment, the FPU(s) 634 also support integercomputation. In one embodiment the FPU(s) 634 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 64-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 635 are also present, and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 608 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can chose theexact number of execution units per sub-core grouping. In one embodimentthe execution unit 608 can execute instructions across a plurality ofexecution channels. In a further embodiment, each thread executed on thegraphics execution unit 608 is executed on a different channel.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 730. The native instructions availablein the 64-bit format 730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format710.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a geometry pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general-purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated logic units (e.g.,L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via adata port 856 to perform memory access and communicate with renderoutput pipeline components of the processor. In some embodiments,sampler 854, caches 851, 858 and execution units 852A-852B each haveseparate memory access paths. In one embodiment the texture cache 858can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front-end 834. In some embodiments, videofront-end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 820 and media pipeline 830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a client902, a command operation code (opcode) 904, and data 906 for thecommand. A sub-opcode 905 and a command size 908 are also included insome commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 9B illustrates an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general-purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1014 in a machinelanguage suitable for execution by the general-purpose processor core1034. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.The integrated circuit package assembly 1170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 1170 includes multiple units ofhardware logic 1172, 1174 connected to a substrate 1180. The logic 1172,1174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 1172, 1174 canbe implemented within a semiconductor die and coupled with the substrate1180 via an interconnect structure 1173. The interconnect structure 1173may be configured to route electrical signals between the logic 1172,1174 and the substrate 1180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 1173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 1172, 1174. In someembodiments, the substrate 1180 is an epoxy-based laminate substrate.The package substrate 1180 may include other suitable types ofsubstrates in other embodiments. The package assembly 1170 can beconnected to other electrical devices via a package interconnect 1183.The package interconnect 1183 may be coupled to a surface of thesubstrate 1180 to route electrical signals to other electrical devices,such as a motherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electricallycoupled with a bridge 1182 that is configured to route electricalsignals between the logic 1172, 1174. The bridge 1182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 1182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 1182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I2S/I2C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIGS. 13A-13B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 13A illustrates an exemplary graphics processor 1310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 13B illustrates anadditional exemplary graphics processor 1340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 1310 of FIG. 13A is anexample of a low power graphics processor core. Graphics processor 1340of FIG. 13B is an example of a higher performance graphics processorcore. Each of the graphics processors 1310, 1340 can be variants of thegraphics processor 1210 of FIG. 12.

As shown in FIG. 13A, graphics processor 1310 includes a vertexprocessor 1305 and one or more fragment processor(s) 1315A-1315N (e.g.,1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphicsprocessor 1310 can execute different shader programs via separate logic,such that the vertex processor 1305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)1315A-1315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 1305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 1315A-1315N usethe primitive and vertex data generated by the vertex processor 1305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 1315A-1315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for the graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12, such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 13B, graphics processor 1340 includes the one or moreMMU(s) 1320A-1320B, caches 1325A-1325B, and circuit interconnects1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphicsprocessor 1340 includes one or more shader core(s) 1355A-1355N (e.g.,1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 1340 includes an inter-core task manager 1345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 1355A-1355N and a tiling unit 1358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

FIGS. 14A-14B illustrate additional exemplary graphics processor logicaccording to embodiments described herein. FIG. 14A illustrates agraphics core 1400 that may be included within the graphics processor1210 of FIG. 12, and may be a unified shader core 1355A-1355N as in FIG.13B. FIG. 14B illustrates an additional highly-parallel general-purposegraphics processing unit 1430, which is a highly-parallelgeneral-purpose graphics processing unit suitable for deployment on amulti-chip module.

As shown in FIG. 14A, the graphics core 1400 includes a sharedinstruction cache 1402, a texture unit 1418, and a cache/shared memory1420 that are common to the execution resources within the graphics core1400. The graphics core 1400 can include multiple slices 1401A-1401N orpartition for each core, and a graphics processor can include multipleinstances of the graphics core 1400. The slices 1401A-1401N can includesupport logic including a local instruction cache 1404A-1404N, a threadscheduler 1406A-1406N, a thread dispatcher 1408A-1408N, and a set ofregisters 1410A-1440N. To perform logic operations, the slices1401A-1401N can include a set of additional function units (AFUs1412A-1412N), floating-point units (FPU 1414A-1414N), integer arithmeticlogic units (ALUs 1416-1416N), address computational units (ACU1413A-1413N), double-precision floating-point units (DPFPU 1415A-1415N),and matrix processing units (MPU 1417A-1417N).

Some of the computational units operate at a specific precision. Forexample, the FPUs 1414A-1414N can perform single-precision (32-bit) andhalf-precision (16-bit) floating point operations, while the DPFPUs1415A-1415N perform double precision (64-bit) floating point operations.The ALUs 1416A-1416N can perform variable precision integer operationsat 8-bit, 16-bit, and 32-bit precision, and can be configured for mixedprecision operations. The MPUs 1417A-1417N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. The MPUs 1417-1417N can perform avariety of matrix operations to accelerate machine learning applicationframeworks, including enabling support for accelerated general matrix tomatrix multiplication (GEMM). The AFUs 1412A-1412N can performadditional logic operations not supported by the floating-point orinteger units, including trigonometric operations (e.g., Sine, Cosine,etc.).

As shown in FIG. 14B, a general-purpose processing unit (GPGPU) 1430 canbe configured to enable highly-parallel compute operations to beperformed by an array of graphics processing units. Additionally, theGPGPU 1430 can be linked directly to other instances of the GPGPU tocreate a multi-GPU cluster to improve training speed for particularlydeep neural networks. The GPGPU 1430 includes a host interface 1432 toenable a connection with a host processor. In one embodiment the hostinterface 1432 is a PCI Express interface. However, the host interfacecan also be a vendor specific communications interface or communicationsfabric. The GPGPU 1430 receives commands from the host processor anduses a global scheduler 1434 to distribute execution threads associatedwith those commands to a set of compute clusters 1436A-1436H. Thecompute clusters 1436A-1436H share a cache memory 1438. The cache memory1438 can serve as a higher-level cache for cache memories within thecompute clusters 1436A-1436H.

The GPGPU 1430 includes memory 14434A-14434B coupled with the computeclusters 1436A-1436H via a set of memory controllers 1442A-1442B. Invarious embodiments, the memory 1434A-1434B can include various types ofmemory devices including dynamic random access memory (DRAM) or graphicsrandom access memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory.

In one embodiment the compute clusters 1436A-1436H each include a set ofgraphics cores, such as the graphics core 1400 of FIG. 14A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for machine learning computations. For example and in oneembodiment at least a subset of the floating point units in each of thecompute clusters 1436A-1436H can be configured to perform 16-bit or32-bit floating point operations, while a different subset of thefloating point units can be configured to perform 64-bit floating pointoperations.

Multiple instances of the GPGPU 1430 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment the multiple instances of the GPGPU 1430 communicate over thehost interface 1432. In one embodiment the GPGPU 1430 includes an I/Ohub 1439 that couples the GPGPU 1430 with a GPU link 1440 that enables adirect connection to other instances of the GPGPU. In one embodiment theGPU link 1440 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 1430. In one embodiment the GPU link 1440 couples with a highspeed interconnect to transmit and receive data to other GPGPUs orparallel processors. In one embodiment the multiple instances of theGPGPU 1430 are located in separate data processing systems andcommunicate via a network device that is accessible via the hostinterface 1432. In one embodiment the GPU link 1440 can be configured toenable a connection to a host processor in addition to or as analternative to the host interface 1432.

While the illustrated configuration of the GPGPU 1430 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 1430 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration the GPGPU 1430 includes fewer of the computeclusters 1436A-1436H relative to the training configuration.Additionally, the memory technology associated with the memory1434A-1434B may differ between inferencing and training configurations,with higher bandwidth memory technologies devoted to trainingconfigurations. In one embodiment the inferencing configuration of theGPGPU 1430 can support inferencing specific instructions. For example,an inferencing configuration can provide support for one or more 8-bitinteger dot product instructions, which are commonly used duringinferencing operations for deployed neural networks.

Ray Tracing with Machine Learning

As mentioned above, ray tracing is a graphics processing technique inwhich a light transport is simulated through physically-based rendering.One of the key operations in ray tracing is processing a visibilityquery which requires traversal and intersection testing of nodes in abounding volume hierarchy (BVH).

Ray- and path-tracing based techniques compute images by tracing raysand paths through each pixel, and using random sampling to computeadvanced effects such as shadows, glossiness, indirect illumination,etc. Using only a few samples is fast but produces noisy images whileusing many samples produces high quality images, but is costprohibitive.

In the last several years, a breakthrough solution to ray-/path-tracingfor real-time use has come in the form of “denoising”—the process ofusing image processing techniques to produce high quality,filtered/denoised images from noisy, low-sample count inputs. The mosteffective denoising techniques rely on machine learning techniques wherea machine-learning engine learns what a noisy image would likely looklike if it had been computed with more samples. In one particularimplementation, the machine learning is performed by a convolutionalneural network (CNN); however, the underlying principles of theinvention are not limited to a CNN implementation. In such animplementation, training data is produced with low-sample count inputsand ground-truth. The CNN is trained to predict the converged pixel froma neighborhood of noisy pixel inputs around the pixel in question.

Though not perfect, this AI-based denoising technique has provensurprisingly effective. The caveat, however, is that good training datais required, since the network may otherwise predict the wrong results.For example, if an animated movie studio trained a denoising CNN on pastmovies with scenes on land and then attempted to use the trained CNN todenoise frames from a new movie set on water, the denoising operationwill perform sub-optimally.

To address this problem, one embodiment of the invention gatherslearning data dynamically, while rendering, and continuously trains amachine learning engine, such as a CNN, based on the data on which it iscurrently being run, thus continuously improving the machine learningengine for the task at hand. This embodiment may still perform atraining phase prior to runtime, but continues to adjust the machinelearning weights as needed during runtime. In addition, this embodimentavoids the high cost of computing the reference data required for thetraining by restricting the generation of learning data to a sub-regionof the image every frame or every N frames. In particular, the noisyinputs of a frame are generated for denoising the full frame with thecurrent network. in addition, a small region of reference pixels aregenerated and used for continuous training, as described below.

While a CNN implementation is described with respect to certainembodiments, any form of machine learning engine may be used including,but not limited to systems which perform supervised learning (e.g.,building a mathematical model of a set of data that contains both theinputs and the desired outputs), unsupervised learning (e.g., whichevaluate the input data for certain types of structure), and/or acombination of supervised and unsupervised learning.

Existing de-noising implementations operate in a training phase and aruntime phase. During the training phase, a network topology is definedwhich receives a region of N×N pixels with various per-pixel datachannels such as pixel color, depth, normal, normal deviation, primitiveIDs, and albedo and generates a final pixel color. A set of“representative” training data is generated using one frame's worth oflow-sample count inputs, and referencing the “desired” pixel colorscomputed with a very high sample count. The network is trained towardsthese inputs, generating a set of “ideal” weights for the network. Inthese implementations, the reference data is used to train the network'sweights to most closely match the network's output to the desiredresult.

At runtime, the given, pre-computed ideal network weights are loaded andthe network is initialized. For each frame, a low-sample count image ofdenoising inputs (i.e., the same as used for training) is generated. Foreach pixel, the given neighborhood of pixels' inputs is run through thenetwork to predict the “denoised” pixel color, generating a denoisedframe.

FIG. 15 illustrates one embodiment of an initial trainingimplementation. A machine learning engine 1500 (e.g., a CNN) receives aregion of N×N pixels as high sample count image data 1702 with variousper-pixel data channels such as pixel color, depth, normal, normaldeviation, primitive IDs, and albedo and generates final pixel colors.Representative training data is generated using one frame's worth oflow-sample count inputs 1501. The network is trained towards theseinputs, generating a set of “ideal” weights 1505 which the machinelearning engine 1500 subsequently uses to denoise low sample countimages at runtime.

To improve the above techniques, one embodiment of the inventionaugments the denoising phase to generate new training data every frameor a subset of frames (e.g., every N frames where N=2, 3, 4, 10, 25,etc). In particular, as illustrated in FIG. 16, this embodiment choosesone or more regions in each frame, referred to here as “new referenceregions” 1602 which are rendered with a high sample count into aseparate high sample count buffer 1604. A low sample count buffer 1603stores the low sample count input frame 1601 (including the low sampleregion 1604 corresponding to the new reference region 1602).

In one embodiment, the location of the new reference region 1602 israndomly selected. Alternatively, the location of the new referenceregion 1602 may be adjusted in a pre-specified manner for each new frame(e.g., using a predefined movement of the region between frames, limitedto a specified region in the center of the frame, etc).

Regardless of how the new reference region is selected, it is used bythe machine learning engine 1600 to continually refine and update thetrained weights 1605 used for denoising. In particular, reference pixelcolors from each new reference region 1602 and noisy reference pixelinputs from a corresponding low sample count region 1607 are rendered.Supplemental training is then performed on the machine learning engine1600 using the high-sample-count reference region 1602 and thecorresponding low sample count region 1607. In contrast to the initialtraining, this training is performed continuously during runtime foreach new reference region 1602—thereby ensuring that the machinelearning engine 1600 is precisely trained. For example, per-pixel datachannels (e.g., pixel color, depth, normal, normal deviation, etc) maybe evaluated, which the machine learning engine 1600 uses to makeadjustments to the trained weights 1605. As in the training case (FIG.15), the machine learning engine 1600 is trained towards a set of idealweights 1605 for removing noise from the low sample count input frame1601 to generate the denoised frame 1620. However, in this embodiment,the trained weights 1605 are continually updated, based on new imagecharacteristics of new types of low sample count input frames 1601.

In one embodiment, the re-training operations performed by the machinelearning engine 1600 are executed concurrently in a background processon the graphics processor unit (GPU) or host processor. The render loop,which may be implemented as a driver component and/or a GPU hardwarecomponent, continuously produces new training data (e.g., in the form ofnew reference regions 1602) which it places in a queue. The backgroundtraining process, executed on the GPU or host processor, continuouslyreads the new training data from this queue, re-trains the machinelearning engine 1600, and updates it with new weights 1605 atappropriate intervals.

FIG. 17 illustrates an example of one such implementation in which thebackground training process 1700 is implemented by the host CPU 1710. Inparticular, in this embodiment, the background training process 1700uses the high sample count new reference region 1602 and thecorresponding low sample region 1604 to continually update the trainedweights 1605, thereby updating the machine learning engine 1600.

As illustrated in FIG. 18A, in one implementation such as in amulti-player online game, different host machines 1820-1822 individuallygenerate reference regions which a background training process 1700A-Ctransmits to a server 1800 (e.g., such as a gaming server). The server1800 then performs training on a machine learning engine 1810 using thenew reference regions received from each of the hosts 1821-1822,updating the weights 1805 as previously described. It transmits theseweights 1805 to the host machines 1820 which store the weights 1605A-C,thereby updating each individual machine learning engine (not shown).Because the server 1800 may be provided a large number of referenceregions in a short period of time, it can efficiently and preciselyupdate the weights for any given application (e.g., an online game)being executed by the users.

As illustrated in FIG. 18B, the different host machines may generate newtrained weights (e.g., based on training/reference regions 1602 aspreviously described) and share the new trained weights with a server1800 (e.g., such as a gaming server) or, alternatively, use apeer-to-peer sharing protocol. A machine learning management component1810 on the server generates a set of combined weights 1805 using thenew weights received from each of the host machines. The combinedweights 1805, for example, may be an average generated from the newweights and continually updated as described herein. Once generated,copies of the combined weights 1605A-C may be transmitted and stored oneach of the host machines 1820-1821 which may then use the combinedweights as described herein to perform de-noising operations.

In one embodiment, this semi-closed loop update mechanism can be used bythe hardware manufacturer. For example, the reference network may beincluded as part of the driver distributed by the hardware manufacturer.As the driver generates new training data using the techniques describedherein and continuously submits these back to the hardware manufacturer,the hardware manufacturer uses this information to continue to improveits machine learning implementations for the next driver update.

In one implementation (e.g., in batch movie rendering on a render farm)the renderer transmits the newly generated training regions to adedicated server or database (in that studio's render farm) thataggregates this data from multiple render nodes over time. A separateprocess on a separate machine continuously improves the studio'sdedicated denoising network, and new render jobs always use the latesttrained network.

A method in accordance with one embodiment of the invention isillustrated in FIG. 19. The method may be implemented on thearchitectures described herein, but is not limited to any particularsystem or graphics processing architecture.

At 1901, as part of the initial training phase, low sample count imagedata and high sample count image data are generated for a plurality ofimage frames. At 1902, a machine-learning denoising engine is trainedusing the high/low sample count image data. In one embodiment, forexample, a set of convolutional neural network weights associated withpixel features may be updated in accordance with the training. However,any machine-learning architecture may be used.

At 1903, at runtime, low sample count image frames are generated alongwith at least one reference region having a high sample count. At 1904,the high sample count reference region is used by the machine-learningengine and/or separate training logic (e.g., background training module1700) to continually refine the training of the machine learning engine.For example, in one embodiment, the high sample count reference regionis used in combination with a corresponding portion of the low samplecount image to continue to teach the machine learning engine 1904 how tomost effectively perform denoising. In a CNN implementation, forexample, this may involve updating the weights associated with the CNN.

Multiple variations of the embodiments described above may beimplemented, such as the manner in which the feedback loop to themachine learning engine is configured, the entities which generate thetraining data, the manner in which the training data is fed back totraining engine, and how the improved network is provided to therendering engines. In addition, while the above embodiments describedabove perform continuous training using a single reference region, anynumber of reference regions may be used. Moreover, as previouslymentioned, the reference regions may be of different sizes, may be usedon different numbers of image frames, and may be positioned in differentlocations within the image frames using different techniques (e.g.,random, according to a predetermined pattern, etc).

In addition, while a convolutional neural network (CNN) is described asone example of a machine-learning engine 1600, the underlying principlesof the invention may be implemented using any form of machine learningengine which is capable of continually refining its results using newtraining data. By way of example, and not limitation, other machinelearning implementations include the group method of data handling(GMDH), long short-term memory, deep reservoir computing, deep beliefnetworks, tensor deep stacking networks, and deep predictive codingnetworks, to name a few.

Apparatus and Method for Efficient Distributed Denoising

As described above, denoising has become a critical feature real-timeray tracing with smooth, noiseless images. Rendering can be done acrossa distributed system on multiple devices, but so far the existingdenoising frameworks all operate on a single instance on a singlemachine. If rendering is being done across multiple devices, they maynot have all rendered pixels accessible for computing a denoised portionof the image.

One embodiment of the invention includes a distributed denoisingalgorithm that works with both artificial intelligence (AI) and non-AIbased denoising techniques. Regions of the image are either alreadydistributed across nodes from a distributed render operation, or splitup and distributed from a single framebuffer. Ghost regions ofneighboring regions needed for computing sufficient denoising arecollected from neighboring nodes when needed, and the final resultingtiles are composited into a final image.

Distributed Processing

FIG. 20 illustrates one embodiment of the invention where multiple nodes2021-2023 perform rendering. While only three nodes are illustrated forsimplicity, the underlying principles of the invention are not limitedto any particular number of nodes. In fact, a single node may be used toimplement certain embodiments of the invention.

Nodes 2021-2023 each render a portion of an image, resulting in regions2011-2013 in this example. While rectangular regions 2011-2013 are shownin FIG. 20, regions of any shape may be used and any device can processany number of regions. The regions that are needed by a node to performa sufficiently smooth denoising operation are referred to as ghostregions 2011-2013. In other words, the ghost regions 2001-2003 representthe entirety of data required to perform denoising at a specified levelof quality. Lowering the quality level reduces the size of the ghostregion and therefore the amount of data required and raising the qualitylevel increases the ghost region and corresponding data required.

In one embodiment, if a node such as node 2021 does have a local copy ofa portion of the ghost region 2001 required to denoise its region 2011at a specified level of quality, the node will retrieve the requireddata from one or more “adjacent” nodes, such as node 2022 which owns aportion of ghost region 2001 as illustrated. Similarly, if node 2022does have a local copy of a portion of ghost region 2002 required todenoise its region 2012 at the specified level of quality, node 2022will retrieve the required ghost region data 2032 from node 2021. Theretrieval may be performed over a bus, an interconnect, a high speedmemory fabric, a network (e.g., high speed Ethernet), or may even be anon-chip interconnect in a multi-core chip capable of distributingrendering work among a plurality of cores (e.g., used for renderinglarge images at either extreme resolutions or time varying). In oneembodiment, each node 2021-2023 comprises an individual execution unitor specified set of execution units within a graphics processor.

The specific amount of data to be sent is dependent on the denoisingtechniques being used. Moreover, the data from the ghost region mayinclude any data needed to improve denoising of each respective region.In one embodiment, for example, the ghost region data includes imagecolors/wavelengths, intensity/alpha data, and/or normals. However, theunderlying principles of the invention are not limited to any particularset of ghost region data.

Additional Details of One Embodiment

For slower networks or interconnects, compression of this data can beutilized using existing general purpose lossless or lossy compression.Examples include, but are not limited to, zlib, gzip, andLempel-Ziv-Markov chain algorithm (LZMA). Further content-specificcompression may be used by noting that the delta in ray hit informationbetween frames can be quite sparse, and only the samples that contributeto that delta need to be sent when the node already has the collecteddeltas from previous frames. These can be selectively pushed to nodesthat collect those samples, i, or node i can request samples from othernodes. In one embodiment, lossless compression is used for certain typesof data and program code while lossy data is used for other types ofdata.

FIG. 21 illustrates additional details of the interactions between nodes2021-2022, in accordance with one embodiment of the invention. Each node2021-2022 includes a ray tracing rendering circuitry 2081-2082 forrendering the respective image regions 2011-2012 and ghost regions2001-2002. Denoisers 2100-2111 execute denoising operations on theregions 2011-2012, respectively, which each node 2021-2022 isresponsible for rendering and denoising. The denoisers 2021-2022, forexample, may comprise circuitry, software, or any combination thereof togenerate the denoised regions 2121-2122, respectively. As mentioned,when generating denoised regions the denoisers 2021-2022 may need torely on data within a ghost region owned by a different node (e.g.,denoiser 2100 may need data from ghost region 2002 owned by node 2022).

Thus, in one embodiment, the denoisers 2100-2111 generate the denoisedregions 2121-2122 using data from regions 2011-2012 and ghost regions2001-2002, respectively, at least a portion of which may be receivedfrom another node. Region data managers 2101-2102 manage data transfersfrom ghost regions 2001-2002 as described herein. In one embodiment,compressor/decompressor units 2131-2132 perform compression anddecompression of the ghost region data exchanged between the nodes2021-2022, respectively.

For example, region data manager 2101 of node 2021 may, upon requestfrom node 2022, send data from ghost region 2001 tocompressor/decompressor 2131, which compresses the data to generatecompressed data 2106 which it transmits to node 2022, thereby reducingbandwidth over the interconnect, network, bus, or other datacommunication link. Compressor/decompressor 2132 of node 2022 thendecompresses the compressed data 2106 and denoiser 2111 uses thedecompressed ghost data to generate a higher quality denoised region2012 than would be possible with only data from region 2012. The regiondata manager 2102 may store the decompressed data from ghost region 2001in a cache, memory, register file or other storage to make it availableto the denoiser 2111 when generating the denoised region 2122. A similarset of operations may be performed to provide the data from ghost region2002 to denoiser 2100 on node 2021 which uses the data in combinationwith data from region 2011 to generate a higher quality denoised region2121.

Grab Data or Render

If the connection between devices such as nodes 2021-2022 is slow (i.e.,lower than a threshold latency and/or threshold bandwidth), it may befaster to render ghost regions locally rather than requesting theresults from other devices. This can be determined at run-time bytracking network transaction speeds and linearly extrapolated rendertimes for the ghost region size. In such cases where it is faster torender out the entire ghost region, multiple devices may end uprendering the same portions of the image. The resolution of the renderedportion of the ghost regions may be adjusted based on the variance ofthe base region and the determined degree of blurring.

Load Balancing

In one embodiment, static and/or dynamic load balancing schemes may areused to distribute the processing load among the various nodes2021-2023. For dynamic load balancing, the variance determined by thedenoising filter may require both more time in denoising but drive theamount of samples used to render a particular region of the scene, withlow variance and blurry regions of the image requiring fewer samples.The specific regions assigned to specific nodes may be adjusteddynamically based on data from previous frames or dynamicallycommunicated across devices as they are rendering so that all deviceswill have the same amount of work.

FIG. 22 illustrates one embodiment in which a monitor 2201-2202 runningon each respective node 2021-2022 collects performance metric dataincluding, but not limited to, the time consumed to transmit data overthe network interface 2211-2212, the time consumed when denoising aregion (with and without ghost region data), and the time consumedrendering each region/ghost region. The monitors 2201-2202 report theseperformance metrics back to a manager or load balancer node 2201, whichanalyzes the data to identify the current workload on each node2021-2022 and potentially determines a more efficient mode of processingthe various denoised regions 2121-2122. The manager node 2201 thendistributes new workloads for new regions to the nodes 2021-2022 inaccordance with the detected load. For example, the manager node 2201may transmit more work to those nodes which are not heavily loadedand/or reallocate work from those nodes which are overloaded. Inaddition, the load balancer node 2201 may transmit a reconfigurationcommand to adjust the specific manner in which rendering and/ordenoising is performed by each of the nodes (some examples of which aredescribed above).

Determining Ghost Regions

In one embodiment, the sizes and shapes of the ghost regions 2001-2002are determined based on the denoising algorithm implemented by thedenoisers 2100-2111. Their respective sizes can then be dynamicallymodified based on the detected variance of the samples being denoised.The learning algorithm used for AI denoising itself may be used fordetermining appropriate region sizes, or in other cases such as abilateral blur the predetermined filter width will determine the size ofthe ghost regions 2001-2002. In an implementation which uses a learningalgorithm, the machine learning engine may be executed on the managernode 2201 and/or portions of the machine learning may be executed oneach of the individual nodes 2021-2023 (see, e.g., FIGS. 18A-B andassociated text above).

Gathering the Final Image

In one embodiment, the final image is generated by gathering therendered and denoised regions from each of the nodes 2021-2023, withoutthe need for the ghost regions or normals. In FIG. 22, for example, thedenoised regions 2121-2122 are transmitted to regions processor 2280 ofthe manager node 2201 which combines the regions to generate the finaldenoised image 2290, which is then displayed on a display 2290. Theregion processor 2280 may combine the regions using a variety of 2Dcompositing techniques. Although illustrated as separate components, theregion processor 2280 and denoised image 2290 may be integral to thedisplay 2290. In this embodiment, the various nodes 2021-2022 may use adirect-send technique to transmit the denoised regions 2121-2122 andpotentially using various lossy or lossless compression of the regiondata.

AI denoising is still a costly operation and as gaming moves into thecloud. As such, distributing processing of denoising across multiplenodes 2021-2022 may become required for achieving real-time frame ratesfor traditional gaming or virtual reality (VR) which requires higherframe rates. Movie studios also often render in large render farms whichcan be utilized for faster denoising.

One embodiment of a method for performing distributed rendering anddenoising is illustrated in FIG. 23. The method may be implementedwithin the context of the system architectures described above, but isnot limited to any particular system architecture.

At 2301, graphics work is dispatched to a plurality of nodes whichperform ray tracing operations to render a region of an image frame. Inone embodiment, each node may already have data required to perform theoperations in memory. For example, two or more of the nodes may share acommon memory or the local memories of the nodes may already have storeddata from prior ray tracing operations. Alternatively, or in addition,certain data may be transmitted to each node.

At 2302, the “ghost region” required for a specified level of denoising(i.e., at an acceptable level of performance) is determined. The ghostregion comprises any data required to perform the specified level ofdenoising, including data owned by one or more other nodes.

At 2303, data related to the ghost regions (or portions thereof) isexchanged between nodes. At 2304 each node performs denoising on itsrespective region (e.g., using the exchanged data) and at 2305 theresults are combined to generate the final denoised image frame.

In one embodiment, a manager node or primary node such as shown in FIG.22 dispatches the work to the nodes and then combines the work performedby the nodes to generate the final image frame. In another embodiment, apeer-based architecture is used where the nodes are peers which exchangedata to render and denoise the final image frame.

The nodes described herein (e.g., nodes 2021-2023) may be graphicsprocessing computing systems interconnected via a high speed network.Alternatively, the nodes may be individual processing elements coupledto a high speed memory fabric. In this embodiment, all of the nodes mayshare a common virtual memory space and/or a common physical memory. Inanother embodiment, the nodes may be a combination of CPUs and GPUs. Forexample, the manager node 2201 described above may be a CPU and/orsoftware executed on the CPU and the nodes 2021-2022 may be GPUs and/orsoftware executed on the GPUs. Various different types of nodes may beused while still complying with the underlying principles of theinvention.

Example Neural Network Implementations

There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 24 is a generalized diagram of a machine learning software stack2400. A machine learning application 2402 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 2402 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 2402can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 2402 can beenabled via a machine learning framework 2404. The machine learningframework 2404 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 2404, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 2404. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 2404 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 2404 can process input data received fromthe machine learning application 2402 and generate the appropriate inputto a compute framework 2406. The compute framework 2406 can abstract theunderlying instructions provided to the GPGPU driver 2408 to enable themachine learning framework 2404 to take advantage of hardwareacceleration via the GPGPU hardware 2410 without requiring the machinelearning framework 2404 to have intimate knowledge of the architectureof the GPGPU hardware 2410. Additionally, the compute framework 2406 canenable hardware acceleration for the machine learning framework 2404across a variety of types and generations of the GPGPU hardware 2410.

GPGPU Machine Learning Acceleration

FIG. 25 illustrates a multi-GPU computing system 2500, according to anembodiment. The multi-GPU computing system 2500 can include a processor2502 coupled to multiple GPGPUs 2506A-D via a host interface switch2504. The host interface switch 2504, in one embodiment, is a PCIexpress switch device that couples the processor 2502 to a PCI expressbus over which the processor 2502 can communicate with the set of GPGPUs2506A-D. Each of the multiple GPGPUs 2506A-D can be an instance of theGPGPU described above. The GPGPUs 2506A-D can interconnect via a set ofhigh-speed point to point GPU to GPU links 2516. The high-speed GPU toGPU links can connect to each of the GPGPUs 2506A-D via a dedicated GPUlink. The P2P GPU links 2516 enable direct communication between each ofthe GPGPUs 2506A-D without requiring communication over the hostinterface bus to which the processor 2502 is connected. With GPU-to-GPUtraffic directed to the P2P GPU links, the host interface bus remainsavailable for system memory access or to communicate with otherinstances of the multi-GPU computing system 2500, for example, via oneor more network devices. While in the illustrated embodiment the GPGPUs2506A-D connect to the processor 2502 via the host interface switch2504, in one embodiment the processor 2502 includes direct support forthe P2P GPU links 2516 and can connect directly to the GPGPUs 2506A-D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIGS. 26-27 illustrate an exemplary convolutional neural network. FIG.26 illustrates various layers within a CNN. As shown in FIG. 26, anexemplary CNN used to model image processing can receive input 2602describing the red, green, and blue (RGB) components of an input image.The input 2602 can be processed by multiple convolutional layers (e.g.,convolutional layer 2604, convolutional layer 2606). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 2608. Neurons in a fully connected layer havefull connections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 2608 can be used to generate an output result from the network.The activations within the fully connected layers 2608 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations are make use of fully connected layers. For example, insome implementations the convolutional layer 2606 can generate outputfor the CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 2608. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 27 illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 2712 of a CNN can beprocessed in three stages of a convolutional layer 2714. The threestages can include a convolution stage 2716, a detector stage 2718, anda pooling stage 2720. The convolution layer 2714 can then output data toa successive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 2716 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 2716 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 2716defines a set of linear activations that are processed by successivestages of the convolutional layer 2714.

The linear activations can be processed by a detector stage 2718. In thedetector stage 2718, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asf(x)=max (0,x), such that the activation is thresholded at zero.

The pooling stage 2720 uses a pooling function that replaces the outputof the convolutional layer 2706 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 2720,including max pooling, average pooling, and l2-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 2714 can then be processed bythe next layer 2722. The next layer 2722 can be an additionalconvolutional layer or one of the fully connected layers 2708. Forexample, the first convolutional layer 2704 of FIG. 27 can output to thesecond convolutional layer 2706, while the second convolutional layercan output to a first layer of the fully connected layers 2808.

FIG. 28 illustrates an exemplary recurrent neural network 2800. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 2800 can bedescribed has having an input layer 2802 that receives an input vector,hidden layers 2804 to implement a recurrent function, a feedbackmechanism 2805 to enable a ‘memory’ of previous states, and an outputlayer 2806 to output a result. The RNN 2800 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 2805. For agiven time step, the state of the hidden layers 2804 is defined by theprevious state and the input at the current time step. An initial input(x1) at a first time step can be processed by the hidden layer 2804. Asecond input (x2) can be processed by the hidden layer 2804 using stateinformation that is determined during the processing of the initialinput (x1). A given state can be computed as s_t=f(Ux_t+Ws_(t−1)), whereU and W are parameter matrices. The function f is generally anonlinearity, such as the hyperbolic tangent function (Tan h) or avariant of the rectifier function f(x)=max (0,x). However, the specificmathematical function used in the hidden layers 2804 can vary dependingon the specific implementation details of the RNN 2800.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 29 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 2902. Various training frameworks2904 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework described above maybe configured as a training framework. The training framework 2904 canhook into an untrained neural network 2906 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 2908.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 2902 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 2904 can adjust to adjust the weights that controlthe untrained neural network 2906. The training framework 2904 canprovide tools to monitor how well the untrained neural network 2906 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 2908. The trained neural network 2908 can then bedeployed to implement any number of machine learning operations.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 2902 will include input data without any associatedoutput data. The untrained neural network 2906 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 2907 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset2902 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 2908 to adapt tothe new data 2912 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 30A is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes such as the nodes described above to perform supervisedor unsupervised training of a neural network. The distributedcomputational nodes can each include one or more host processors and oneor more of the general-purpose processing nodes, such as ahighly-parallel general-purpose graphics processing unit. Asillustrated, distributed learning can be performed model parallelism3002, data parallelism 3004, or a combination of model and dataparallelism.

In model parallelism 3002, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 3004, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 3006 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit and/or themulti-GPU computing systems described herein. On the contrary, deployedmachine learning platforms generally include lower power parallelprocessors suitable for use in products such as cameras, autonomousrobots, and autonomous vehicles.

FIG. 30B illustrates an exemplary inferencing system on a chip (SOC)3100 suitable for performing inferencing using a trained model. The SOC3100 can integrate processing components including a media processor3102, a vision processor 3104, a GPGPU 3106 and a multi-core processor3108. The SOC 3100 can additionally include on-chip memory 3105 that canenable a shared on-chip data pool that is accessible by each of theprocessing components. The processing components can be optimized forlow power operation to enable deployment to a variety of machinelearning platforms, including autonomous vehicles and autonomous robots.For example, one implementation of the SOC 3100 can be used as a portionof the main control system for an autonomous vehicle. Where the SOC 3100is configured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 3102 and vision processor 3104 canwork in concert to accelerate computer vision operations. The mediaprocessor 3102 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 3105. The vision processor 3104 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 3104 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 3106.

The multi-core processor 3108 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 3102 and the visionprocessor 3104. The multi-core processor 3108 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 3106. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 3108. Such softwarecan directly issue computational workloads to the GPGPU 3106 or thecomputational workloads can be issued to the multi-core processor 3108,which can offload at least a portion of those operations to the GPGPU3106.

The GPGPU 3106 can include compute clusters such as a low powerconfiguration of the compute clusters DPLAB06A-DPLAB06H within thehighly-parallel general-purpose graphics processing unit DPLAB00. Thecompute clusters within the GPGPU 3106 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 3106 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

Ray Tracing Architecture

In one implementation, the graphics processor includes circuitry and/orprogram code for performing real-time ray tracing. In some embodiments,a dedicated set of ray tracing cores are included in the graphicsprocessor to perform the various ray tracing operations describedherein, including ray traversal and/or ray intersection operations. Inaddition to the ray tracing cores, one embodiment includes multiple setsof graphics processing cores for performing programmable shadingoperations and multiple sets of tensor cores for performing matrixoperations on tensor data.

FIG. 31 illustrates an exemplary portion of one such graphics processingunit (GPU) 3105 which includes dedicated sets of graphics processingresources arranged into multi-core groups 3100A-N. While the details ofonly a single multi-core group 3100A are provided, it will beappreciated that the other multi-core groups 3100B-N may be equippedwith the same or similar sets of graphics processing resources.

As illustrated, a multi-core group 3100A may include a set of graphicscores 3130, a set of tensor cores 3140, and a set of ray tracing cores3150. A scheduler/dispatcher 3110 schedules and dispatches the graphicsthreads for execution on the various cores 3130, 3140, 3150. A set ofregister files 3120 store operand values used by the cores 3130, 3140,3150 when executing the graphics threads. These may include, forexample, integer registers for storing integer values, floating pointregisters for storing floating point values, vector registers forstoring packed data elements (integer and/or floating point dataelements) and tile registers for storing tensor/matrix values. In oneembodiment, the tile registers are implemented as combined sets ofvector registers.

One or more Level 1 (L1) caches and texture units 3160 store graphicsdata such as texture data, vertex data, pixel data, ray data, boundingvolume data, etc, locally within each multi-core group 3100A. A Level 2(L2) cache 3180 shared by all or a subset of the multi-core groups3100A-N stores graphics data and/or instructions for multiple concurrentgraphics threads. As illustrated, the L2 cache 3180 may be shared acrossa plurality of multi-core groups 3100A-N. One or more memory controllers3170 couple the GPU 3105 to a memory 3198 which may be a system memory(e.g., DRAM) and/or a dedicated graphics memory (e.g., GDDR6 memory).

Input/output (IO) circuitry 3195 couples the GPU 3105 to one or more IOdevices 3195 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 3190 to the GPU 3105 and memory 3198. One ormore IO memory management units (IOMMUs) 3170 of the IO circuitry 3195couple the IO devices 3190 directly to the system memory 3198. In oneembodiment, the IOMMU 3170 manages multiple sets of page tables to mapvirtual addresses to physical addresses in system memory 3198. In thisembodiment, the IO devices 3190, CPU(s) 3199, and GPU(s) 3105 may sharethe same virtual address space.

In one implementation, the IOMMU 3170 supports virtualization. In thiscase, it may manage a first set of page tables to map guest/graphicsvirtual addresses to guest/graphics physical addresses and a second setof page tables to map the guest/graphics physical addresses tosystem/host physical addresses (e.g., within system memory 3198). Thebase addresses of each of the first and second sets of page tables maybe stored in control registers and swapped out on a context switch(e.g., so that the new context is provided with access to the relevantset of page tables). While not illustrated in FIG. 31, each of the cores3130, 3140, 3150 and/or multi-core groups 3100A-N may includetranslation lookaside buffers (TLBs) to cache guest virtual to guestphysical translations, guest physical to host physical translations, andguest virtual to host physical translations.

In one embodiment, the CPUs 3199, GPUs 3105, and IO devices 3190 areintegrated on a single semiconductor chip and/or chip package. Theillustrated memory 3198 may be integrated on the same chip or may becoupled to the memory controllers 3170 via an off-chip interface. In oneimplementation, the memory 3198 comprises GDDR6 memory which shares thesame virtual address space as other physical system-level memories,although the underlying principles of the invention are not limited tothis specific implementation.

In one embodiment, the tensor cores 3140 include a plurality ofexecution units specifically designed to perform matrix operations,which are the fundamental compute operation used to perform deeplearning operations. For example, simultaneous matrix multiplicationoperations may be used for neural network training and inferencing. Thetensor cores 3140 may perform matrix processing using a variety ofoperand precisions including single precision floating-point (e.g., 32bits), half-precision floating point (e.g., 16 bits), integer words (16bits), bytes (8 bits), and half-bytes (4 bits). In one embodiment, aneural network implementation extracts features of each rendered scene,potentially combining details from multiple frames, to construct ahigh-quality final image.

In deep learning implementations, parallel matrix multiplication workmay be scheduled for execution on the tensor cores 3140. The training ofneural networks, in particular, requires a significant number matrix dotproduct operations. In order to process an inner-product formulation ofan N×N×N matrix multiply, the tensor cores 3140 may include at least Ndot-product processing elements. Before the matrix multiply begins, oneentire matrix is loaded into tile registers and at least one column of asecond matrix is loaded each cycle for N cycles. Each cycle, there are Ndot products that are processed.

Matrix elements may be stored at different precisions depending on theparticular implementation, including 16-bit words, 8-bit bytes (e.g.,INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes maybe specified for the tensor cores 3140 to ensure that the most efficientprecision is used for different workloads (e.g., such as inferencingworkloads which can tolerate quantization to bytes and half-bytes).

In one embodiment, the ray tracing cores 3150 accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. In particular, the ray tracing cores 3150 include raytraversal/intersection circuitry for performing ray traversal usingbounding volume hierarchies (BVHs) and identifying intersections betweenrays and primitives enclosed within the BVH volumes. The ray tracingcores 3150 may also include circuitry for performing depth testing andculling (e.g., using a Z buffer or similar arrangement). In oneimplementation, the ray tracing cores 3150 perform traversal andintersection operations in concert with the image denoising techniquesdescribed herein, at least a portion of which may be executed on thetensor cores 3140. For example, in one embodiment, the tensor cores 3140implement a deep learning neural network to perform denoising of framesgenerated by the ray tracing cores 3150. However, the CPU(s) 3199,graphics cores 3130, and/or ray tracing cores 3150 may also implementall or a portion of the denoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising maybe employed in which the GPU 3105 is in a computing device coupled toother computing devices over a network or high speed interconnect. Inthis embodiment, the interconnected computing devices share neuralnetwork learning/training data to improve the speed with which theoverall system learns to perform denoising for different types of imageframes and/or different graphics applications.

In one embodiment, the ray tracing cores 3150 process all BVH traversaland ray-primitive intersections, saving the graphics cores 3130 frombeing overloaded with thousands of instructions per ray. In oneembodiment, each ray tracing core 3150 includes a first set ofspecialized circuitry for performing bounding box tests (e.g., fortraversal operations) and a second set of specialized circuitry forperforming the ray-triangle intersection tests (e.g., intersecting rayswhich have been traversed). Thus, in one embodiment, the multi-coregroup 3100A can simply launch a ray probe, and the ray tracing cores3150 independently perform ray traversal and intersection and return hitdata (e.g., a hit, no hit, multiple hits, etc) to the thread context.The other cores 3130, 3140 are freed to perform other graphics orcompute work while the ray tracing cores 3150 perform the traversal andintersection operations.

In one embodiment, each ray tracing core 3150 includes a traversal unitto perform BVH testing operations and an intersection unit whichperforms ray-primitive intersection tests. The intersection unitgenerates a “hit”, “no hit”, or “multiple hit” response, which itprovides to the appropriate thread. During the traversal andintersection operations, the execution resources of the other cores(e.g., graphics cores 3130 and tensor cores 3140) are freed to performother forms of graphics work.

In one particular embodiment described below, a hybrid rasterization/raytracing approach is used in which work is distributed between thegraphics cores 3130 and ray tracing cores 3150.

In one embodiment, the ray tracing cores 3150 (and/or other cores 3130,3140) include hardware support for a ray tracing instruction set such asMicrosoft's DirectX Ray Tracing (DXR) which includes a DispatchRayscommand, as well as ray-generation, closest-hit, any-hit, and missshaders, which enable the assignment of unique sets of shaders andtextures for each object. Another ray tracing platform which may besupported by the ray tracing cores 3150, graphics cores 3130 and tensorcores 3140 is Vulkan 1.1.85. Note, however, that the underlyingprinciples of the invention are not limited to any particular raytracing ISA.

In general, the various cores 3150, 3140, 3130 may support a ray tracinginstruction set that includes instructions/functions for ray generation,closest hit, any hit, ray-primitive intersection, per-primitive andhierarchical bounding box construction, miss, visit, and exceptions.More specifically, one embodiment includes ray tracing instructions toperform the following functions:

Ray Generation—Ray generation instructions may be executed for eachpixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate theclosest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections betweena ray and primitives within a scene, potentially to identify a newclosest intersection point.

Intersection—An intersection instruction performs a ray-primitiveintersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds abounding box around a given primitive or group of primitives (e.g., whenbuilding a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, orspecified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invokedfor various error conditions).

Hierarchical Beam Tracing

Bounding volume hierarchies are commonly used to improve the efficiencywith which operations are performed on graphics primitives and othergraphics objects. A BVH is a hierarchical tree structure which is builtbased on a set of geometric objects. At the top of the tree structure isthe root node which encloses all of the geometric objects in a givenscene. The individual geometric objects are wrapped in bounding volumesthat form the leaf nodes of the tree. These nodes are then grouped assmall sets and enclosed within larger bounding volumes. These, in turn,are also grouped and enclosed within other larger bounding volumes in arecursive fashion, eventually resulting in a tree structure with asingle bounding volume, represented by the root node, at the top of thetree. Bounding volume hierarchies are used to efficiently support avariety of operations on sets of geometric objects, such as collisiondetection, primitive culling, and ray traversal/intersection operationsused in ray tracing.

In ray tracing architectures, rays are traversed through a BVH todetermine ray-primitive intersections. For example, if a ray does notpass through the root node of the BVH, then the ray does not intersectany of the primitives enclosed by the BVH and no further processing isrequired for the ray with respect to this set of primitives. If a raypasses through a first child node of the BVH but not the second childnode, then the ray need not be tested against any primitives enclosed bythe second child node. In this manner, a BVH provides an efficientmechanism to test for ray-primitive intersections.

In one embodiment of the invention, groups of contiguous rays, referredto as “beams” are tested against the BVH, rather than individual rays.FIG. 32 illustrates an exemplary beam 3201 outlined by four differentrays. Any rays which intersect the patch 3200 defined by the four raysare considered to be within the same beam. While the beam 3201 in FIG.32 is defined by a rectangular arrangement of rays, beams may be definedin various other ways while still complying with the underlyingprinciples of the invention (e.g., circles, ellipses, etc).

FIG. 33 illustrates an exemplary embodiment in which a ray tracingengine 3310 of a GPU 3320 implements the beam tracing techniquesdescribed herein. In particular, ray generation circuitry 3304 generatesa plurality of rays for which traversal and intersection operations areto be performed. However, rather than performing traversal anintersection operations on individual rays, the illustrated embodimentperforms traversal and intersection using a hierarchy of beams 3307generated by beam hierarchy construction circuitry 3305. In oneembodiment, the beam hierarchy is analogous to the bounding volumehierarchy (BVH). For example, FIG. 34 provides an example of a primarybeam 3400 which may be subdivided into a plurality of differentcomponents. In particular, primary beam 3400 may be divided intoquadrants 3401-3404 and each quadrant may itself be divided intosub-quadrants such as sub-quadrants A-D within quadrant 3404. Theprimary beam may be subdivided in a variety of ways. For example, in oneembodiment, the primary beam may be divided in half (rather thanquadrants) and each half may be divided in half, and so on. Regardlessof how the subdivisions are made, in one embodiment, a hierarchicalstructure is generated in a similar manner as a BVH, e.g., with a rootnode representing the primary beam 3400, a first level of child nodes,each represented by a quadrant 3401-3404, second level child nodes foreach sub-quadrant A-D, and so on.

In one embodiment, once the beam hierarchy 3307 is constructed,traversal/intersection circuitry 3306 performs traversal/intersectionoperations using the beam hierarchy 3307 and the BVH 3308. Inparticular, it may test the beam against the BVH and cull portions ofthe beam which do not intersect any portions of the BVH. Using the datashown in FIG. 34, for example, if the sub-beams associated withsub-regions 3402 and 3403 do not intersect with the BVH or a particularbranch of the BVH, then they may be culled with respect to the BVH orthe branch. The remaining portions 3401, 3404 may be tested against theBVH by performing a depth-first search or other search algorithm.

A method in accordance with one embodiment of the invention isillustrated in FIG. 35. The method may be implemented within the contextof the graphics processing architectures described above, but is notlimited to any particular architecture.

At 3500 a primary beam is constructed comprising a plurality of rays andat 3501, the beam is subdivided and hierarchical data structuresgenerated to create a beam hierarchy. In one embodiment, operations3500-3501 are performed as a single, integrated operation whichconstructs a beam hierarchy from a plurality of rays. at 3502, the beamhierarchy is used with a BVH to cull rays (from the beam hierarchy)and/or nodes/primitives from the BVH. At 3503, ray-primitiveintersections are determined for the remaining rays and primitives.

Lossy and Lossless Packet Compression in a Distributed Ray TracingSystem

In one embodiment, ray tracing operations are distributed across aplurality of compute nodes coupled together over a network. FIG. 36, forexample, illustrates a ray tracing cluster 3600 comprising a pluralityof ray tracing nodes 3610-3613 perform ray tracing operations inparallel, potentially combining the results on one of the nodes. In theillustrated architecture, the ray tracing nodes 3610-3613 arecommunicatively coupled to a client-side ray tracing application 3630via a gateway.

One of the difficulties with a distributed architecture is the largeamount of packetized data that must be transmitted between each of theray tracing nodes 3610-3613. In one embodiment, both losslesscompression techniques and lossy compression techniques are used toreduce the data transmitted between the ray tracing nodes 3610-3613.

To implement lossless compression, rather than sending packets filledwith the results of certain types of operations, data or commands aresent which allow the receiving node to reconstruct the results. Forexample, stochastically sampled area lights and ambient occlusion (AO)operations do not necessarily need directions. Consequently, in oneembodiment, a transmitting node will simply send a random seed which isthen used by the receiving node to perform random sampling. For example,if a scene is distributed across nodes 3610-3612, to sample light 1 atpoints p1-p3, only the light ID and origins need to be sent to nodes3610-3612. Each of the nodes may then stochastically sample the lightindependently. In one embodiment, the random seed is generated by thereceiving node. Similarly, for primary ray hit points, ambient occlusion(AO) and soft shadow sampling can be computed on nodes 3610-3612 withoutwaiting for the original points for successive frames. Additionally, ifit is known that a set of rays will go to the same point light source,instructions may be sent identifying the light source to the receivingnode which will apply it to the set of rays. As another example, ifthere are N ambient occlusion rays transmitted a single point, a commandmay be sent to generate N samples from this point.

Various additional techniques may be applied for lossy compression. Forexample, in one embodiment, a quantization factor may be employed toquantize all coordinate values associated with the BVH, primitives, andrays. In addition, 32-bit floating point values used for data such asBVH nodes and primitives may be converted into 8-bit integer values. Inone particular implementation, the bounds of ray packets are stored inin full precision but individual ray points P1-P3 are transmitted asindexed offsets to the bounds. Similarly, a plurality of localcoordinate systems may be generated which use 8-bit integer values aslocal coordinates. The location of the origin of each of these localcoordinate systems may be encoded using the full precision (e.g., 32-bitfloating point) values, effectively connecting the global and localcoordinate systems.

The following is an example of lossless compression employed in oneembodiment of the invention. A n example of a Ray data format usedinternally in a ray tracing program is as follows:

struct Ray

{

-   -   uint32 pixId;    -   uint32 materialID;    -   uint32 instanceID;    -   uint64 primitiveID;    -   uint32 geometryID;    -   uint32 lightID;    -   float origin[3];    -   float direction[3];    -   float t0;    -   float t;    -   float time;    -   float normal[3]; //used for geometry intersections    -   float u;    -   float v;    -   float wavelength;    -   float phase; //Interferometry    -   float refractedOffset; //Schlieren-esque    -   float amplitude;    -   float weight;

};

Instead of sending the raw data for each and every node generated, thisdata can be compressed by grouping values and by creating implicit raysusing applicable metadata where possible.

Bundling and Grouping Ray Data

One embodiment uses flags for common data or masks with modifiers.

struct RayPacket

{

-   -   uint32 size;    -   uint32 flags;    -   list<Ray> rays;

}

For example:

RayPacket.rays=ray_1 to ray_256

Origins are all Shared

All ray data is packed, except only a single origin is stored across allrays. RayPacket.flags is set for RAYPACKET_COMMON_ORIGIN. When RayPacketis unpacked when received, origins are filled in from the single originvalue.

Origins are Shared Only Among Some Rays

All ray data is packed, except for rays that share origins. For eachgroup of unique shared origins, an operator is packed on that identifiesthe operation (shared origins), stores the origin, and masks which raysshare the information. Such an operation can be done on any sharedvalues among nodes such as material IDs, primitive IDs, origin,direction, normals, etc.

struct RayOperation

{

-   -   uint8 operationID;    -   void* value;    -   uint64 mask;

}

Sending Implicit Rays

Often times, ray data can be derived on the receiving end with minimalmeta information used to generate it. A very common example isgenerating multiple secondary rays to stochastically sample an area.Instead of the sender generating a secondary ray, sending it, and thereceiver operating on it, the sender can send a command that a ray needsto be generated with any dependent information, and the ray is generatedon the receiving end. In the case where the ray needs to be firstgenerated by the sender to determine which receiver to send it to, theray is generated and the random seed can be sent to regenerate the exactsame ray.

For example, to sample a hit point with 64 shadow rays sampling an arealight source, all 64 rays intersect with regions from the same computeN4. A RayPacket with common origin and normal is created. More datacould be sent if one wished the receiver to shade the resulting pixelcontribution, but for this example let us assume we wish to only returnwhether a ray hits another nodes data. A RayOperation is created for agenerate shadow ray operation, and is assigned the value of the lightIDto be sampled and the random number seed. When N4 receives the raypacket, it generates the fully filled Ray data by filling in the sharedorigin data to all rays and setting the direction based on the lightIDstochastically sampled with the random number seed to generate the samerays that the original sender generated. When the results are returned,only binary results for every ray need be returned, which can be handedby a mask over the rays.

Sending the original 64 rays in this example would have used 104Bytes*64 rays=6656 Bytes. If the returning rays were sent in their rawform as well, than this is also doubled to 13312 Bytes. Using losslesscompression with only sending the common ray origin, normal, and raygeneration operation with seed and ID, only 29 Bytes are sent with 8Bytes returned for the was intersected mask. This results in a datacompression rate that needs to be sent over the network of ˜360:1. Thisdoes not include overhead to process the message itself, which wouldneed to be identified in some way, but that is left up to theimplementation. Other operations may be done for recomputing ray originand directions from the pixeID for primary rays, recalculating pixelIDsbased on the ranges in the raypacket, and many other possibleimplementations for recomputation of values. Similar operations can beused for any single or group of rays sent, including shadows,reflections, refraction, ambient occlusion, intersections, volumeintersections, shading, bounced reflections in path tracing, etc.

FIG. 37 illustrates additional details for two ray tracing nodes3710-3711 which perform compression and decompression of ray tracingpackets. In particular, in one embodiment, when a first ray tracingengine 3730 is ready to transmit data to a second ray tracing engine3731, ray compression circuitry 3720 performs lossy and/or losslesscompression of the ray tracing data as described herein (e.g.,converting 32-bit values to 8-bit values, substituting raw data forinstructions to reconstruct the data, etc). The compressed ray packets3701 are transmitted from network interface 3725 to network interface3726 over a local network (e.g., a 10 Gb/s, 100 Gb/s Ethernet network).Ray decompression circuitry then decompresses the ray packets whenappropriate. For example, it may execute commands to reconstruct the raytracing data (e.g., using a random seed to perform random sampling forlighting operations). Ray tracing engine 3731 then uses the receiveddata to perform ray tracing operations.

In the reverse direction, ray compression circuitry 3741 compresses raydata, network interface 3726 transmits the compressed ray data over thenetwork (e.g., using the techniques described herein), ray decompressioncircuitry 3740 decompresses the ray data when necessary and ray tracingengine 3730 uses the data in ray tracing operations. Althoughillustrated as a separate unit in FIG. 37, ray decompression circuitry3740-3741 may be integrated within ray tracing engines 3730-3731,respectively. For example, to the extent the compressed ray datacomprises commands to reconstruct the ray data, these commands may beexecuted by each respective ray tracing engine 3730-3731.

As illustrated in FIG. 38, ray compression circuitry 3720 may includelossy compression circuitry 3801 for performing the lossy compressiontechniques described herein (e.g., converting 32-bit floating pointcoordinates to 8-bit integer coordinates) and lossless compressioncircuitry 3803 for performing the lossless compression techniques (e.g.,transmitting commands and data to allow ray recompression circuitry 3821to reconstruct the data). Ray decompression circuitry 3721 includeslossy decompression circuitry 3802 and lossless decompression circuitry3804 for performing lossless decompression.

A method in accordance with one embodiment is illustrated in FIG. 39.The method may be implemented on the ray tracing architectures describedherein but is not limited to any particular architecture.

At 3900, ray data is received which will be transmitted from a first raytracing node to a second ray tracing node. At 3901, lossy compressioncircuitry performs lossy compression on first ray tracing data and, at3902, lossless compression circuitry performs lossless compression onsecond ray tracing data. At 3903, the compressed ray racing data istransmitted to a second ray tracing node. At 3904, lossy/losslessdecompression circuitry performs lossy/lossless decompression of the raytracing data and, at 3905, the second ray tracing node performs raytracing operations sing the decompressed data.

Graphics Processor with Hardware Accelerated Hybrid Ray Tracing

One embodiment of the invention includes a hybrid rendering pipelinewhich performs rasterization on graphics cores 3130 and ray tracingoperations on the ray tracing cores 3150, graphics cores 3130, and/orCPU 3199 cores. For example, rasterization and depth testing may beperformed on the graphics cores 3130 in place of the primary ray castingstage. The ray tracing cores 3150 may then generate secondary rays forray reflections, refractions, and shadows. In addition, certainembodiments may select certain regions of a scene in which the raytracing cores 3150 will perform ray tracing operations (e.g., based onmaterial property thresholds such as high reflectivity levels) whileother regions of the scene will be rendered with rasterization on thegraphics cores 3130. In one embodiment, this hybrid implementation isused for real-time ray tracing applications—where latency is a criticalissue.

One embodiment of the ray traversal architecture described belowperforms programmable shading and control of ray traversal usingexisting single instruction multiple data (SIMD) and/or singleinstruction multiple thread (SIMT) graphics processors whileaccelerating critical functions, such as BVH traversal and/orintersections, using dedicated hardware. In this embodiment, SIMDoccupancy for incoherent paths is improved by regrouping spawned shadersat specific points during traversal and before shading. This is achievedusing dedicated hardware that sorts shaders dynamically, on-chip.Recursion is managed by splitting a function into continuations thatexecute upon returning and regrouping continuations before execution forimproved SIMD occupancy.

Programmable control of ray traversal/intersection is achieved bydecomposing traversal functionality into an inner traversal that can beimplemented as fixed function hardware and an outer traversal thatexecutes on GPU processors and enables programmable control through userdefined traversal shaders. The cost of transferring the traversalcontext between hardware and software is reduced by conservativelytruncating the inner traversal state during the transition between innerand outer traversal.

Programmable control of ray tracing can be expressed through thedifferent shader types listed in Table A below. There can be multipleshaders for each type. For example each material can have a differenthit shader.

TABLE A Shader Type Functionality Primary Launching primary rays HitBidirectional reflectance distribution function (BRDF) sampling,launching secondary rays Any Hit Computing transmittance for alphatextured geometry Miss Computing radiance from a light sourceIntersection Intersecting custom shapes Traversal Instance selection andtransformation Callable A general-purpose function

In one embodiment, recursive ray tracing is initiated by an API functionthat commands the graphics processor to launch a set of primary shadersor intersection circuitry which can spawn ray-scene intersections forprimary rays. This in turn spawns other shaders such as traversal, hitshaders, or miss shaders. A shader that spawns a child shader can alsoreceive a return value from that child shader. Callable shaders aregeneral-purpose functions that can be directly spawned by another shaderand can also return values to the calling shader.

FIG. 40 illustrates an embodiment of a graphics processing architecturewhich includes shader execution circuitry 4000 and fixed functioncircuitry 4010. The general purpose execution hardware subsystemincludes a plurality of single instruction multiple data (SIMD) and/orsingle instructions multiple threads (SIMT) cores/execution units (EUs)4001 (i.e., each core may comprise a plurality of execution units), oneor more samplers 4002, and a Level 1 (L1) cache 4003 or other form oflocal memory. The fixed function hardware subsystem 4010 includesmessage unit 4004, a scheduler 4007, ray-BVH traversal/intersectioncircuitry 4005, sorting circuitry 4008, and a local L1 cache 4006.

In operation, primary dispatcher 4009 dispatches a set of primary raysto the scheduler 4007, which schedules work to shaders executed on theSIMD/SIMT cores/EUs 4001. The SIMD cores/EUs 4001 may be ray tracingcores 3150 and/or graphics cores 3130 described above. Execution of theprimary shaders spawns additional work to be performed (e.g., to beexecuted by one or more child shaders and/or fixed function hardware).The message unit 4004 distributes work spawned by the SIMD cores/EUs4001 to the scheduler 4007, accessing the free stack pool as needed, thesorting circuitry 4008, or the ray-BVH intersection circuitry 4005. Ifthe additional work is sent to the scheduler 4007, it is scheduled forprocessing on the SIMD/SIMT cores/EUs 4001. Prior to scheduling, thesorting circuitry 4008 may sort the rays into groups or bins asdescribed herein (e.g., grouping rays with similar characteristics). Theray-BVH intersection circuitry 4005 performs intersection testing ofrays using BVH volumes. For example, the ray-BVH intersection circuitry4005 may compare ray coordinates with each level of the BVH to identifyvolumes which are intersected by the ray.

Shaders can be referenced using a shader record, a user-allocatedstructure that includes a pointer to the entry function, vendor-specificmetadata, and global arguments to the shader executed by the SIMDcores/EUs 4001. Each executing instance of a shader is associated with acall stack which may be used to store arguments passed between a parentshader and child shader. Call stacks may also store references to thecontinuation functions that are executed when a call returns.

FIG. 41 illustrates an example set of assigned stacks 4101 whichincludes a primary shader stack, a hit shader stack, a traversal shaderstack, a continuation function stack, and a ray-BVH intersection stack(which, as described, may be executed by fixed function hardware 4010).New shader invocations may implement new stacks from a free stack pool4102. The call stacks may be cached in a local L1 cache 4003, 4006 toreduce the latency of accesses.

In one embodiment, there are a finite number of call stacks, each with afixed maximum size “Sstack” allocated in a contiguous region of memory.Therefore the base address of a stack can be directly computed from astack index (SID) as base address=SID*Sstack. In one embodiment, stackIDs are allocated and deallocated by the scheduler 4007 when schedulingwork to the SIMD cores/EUs 4001.

In one embodiment, the primary dispatcher 4009 comprises a graphicsprocessor command processor which dispatches primary shaders in responseto a dispatch command from the host (e.g., a CPU). The scheduler 4007receives these dispatch requests and launches a primary shader on a SIMDprocessor thread if it can allocate a stack ID for each SIMD lane. StackIDs are allocated from the free stack pool 4102 that is initialized atthe beginning of the dispatch command.

An executing shader can spawn a child shader by sending a spawn messageto the messaging unit 4004. This command includes the stack IDsassociated with the shader and also includes a pointer to the childshader record for each active SIMD lane. A parent shader can only issuethis message once for an active lane. In one embodiment, after sendingspawn messages for all relevant lanes, the parent shader terminates.

A shader executed on the SIMD cores/EUs 4001 can also spawnfixed-function tasks such as ray-BVH intersections using a spawn messagewith a shader record pointer reserved for the fixed-function hardware.As mentioned, the messaging unit 4004 sends spawned ray-BVH intersectionwork to the fixed-function ray-BVH intersection circuitry 4005 andcallable shaders directly to the sorting circuitry 4008. In oneembodiment, the sorting circuitry groups the shaders by shader recordpointer to derive a SIMD batch with similar characteristics.Accordingly, stack IDs from different parent shaders can be grouped bythe sorting circuitry 4008 in the same batch. The sorting circuitry 4008sends grouped batches to the scheduler 4007 which accesses the shaderrecord from graphics memory 2511 or the last level cache (LLC) 4020 andlaunches the shader on a processor thread.

In one embodiment, continuations are treated as callable shaders and mayalso be referenced through shader records. When a child shader isspawned and returns values to the parent shader, a pointer to thecontinuation shader record is pushed on the call stack 4101. When achild shader returns, the continuation shader record is popped from thecall stack 4101 and a continuation shader is spawned. Spawnedcontinuations go through the sorting unit similar to callable shadersand get launched on a processor thread.

As illustrated in FIG. 42, one embodiment of the sorting circuitry 4008groups spawned tasks by shader record pointers 4201A, 4201B, 4201 n tocreate SIMD batches for shading. The stack IDs or context IDs in asorted batch can be grouped from different dispatches and differentinput SIMD lanes. In one embodiment, grouping circuitry 4210 performsthe sorting using a content addressable memory (CAM) structure 4201comprising a plurality of entries with each entry identified with a tag4201. As mentioned, in one embodiment, the tag 4201 is a correspondingshader record pointer 4201A, 4201B, 4201 n. In one embodiment, the CAMstructure 4201 stores a limited number of tags (e.g. 32, 64, 128, etc)each associated with an incomplete SIMD batch corresponding to a shaderrecord pointer.

For an incoming spawn command, each SIMD lane has a corresponding stackID (shown as 16 context IDs 0-15 in each CAM entry) and a shader recordpointer 4201A-B, . . . n (acting as a tag value). In one embodiment, thegrouping circuitry 4210 compares the shader record pointer for each laneagainst the tags 4201 in the CAM structure 4201 to find a matchingbatch. If a matching batch is found, the stack ID/context ID is added tothe batch. Otherwise a new entry with a new shader record pointer tag iscreated, possibly evicting an older entry with an incomplete batch.

An executing shader can deallocate the call stack when it is empty bysending a deallocate message to the message unit. The deallocate messageis relayed to the scheduler which returns stack IDs/context IDs foractive SIMD lanes to the free pool.

One embodiment of the invention implements a hybrid approach for raytraversal operations, using a combination of fixed-function raytraversal and software ray traversal. Consequently, it provides theflexibility of software traversal while maintaining the efficiency offixed-function traversal. FIG. 43 shows an acceleration structure whichmay be used for hybrid traversal, which is a two-level tree with asingle top level BVH 4300 and several bottom level BVHs 4301 and 4302.Graphical elements are shown to the right to indicate inner traversalpaths 4303, outer traversal paths 4304, traversal nodes 4305, leaf nodeswith triangles 4306, and leaf nodes with custom primitives 4307.

The leaf nodes with triangles 4306 in the top level BVH 4300 canreference triangles, intersection shader records for custom primitivesor traversal shader records. The leaf nodes with triangles 4306 of thebottom level BVHs 4301-4302 can only reference triangles andintersection shader records for custom primitives. The type of referenceis encoded within the leaf node 4306. Inner traversal 4303 refers totraversal within each BVH 4300-4302. Inner traversal operations comprisecomputation of ray-BVH intersections and traversal across the BVHstructures 4300-4302 is known as outer traversal. Inner traversaloperations can be implemented efficiently in fixed function hardwarewhile outer traversal operations can be performed with acceptableperformance with programmable shaders. Consequently, one embodiment ofthe invention performs inner traversal operations using fixed-functioncircuitry 4010 and performs outer traversal operations using the shaderexecution circuitry 4000 including SIMD/SIMT cores/EUs 4001 forexecuting programmable shaders.

Note that the SIMD/SIMT cores/EUs 4001 are sometimes simply referred toherein as “cores,” “SIMD cores,” “EUs,” or “SIMD processors” forsimplicity. Similarly, the ray-BVH traversal/intersection circuitry 4005is sometimes simply referred to as a “traversal unit,”“traversal/intersection unit” or “traversal/intersection circuitry.”When an alternate term is used, the particular name used to designatethe respective circuitry/logic does not alter the underlying functionswhich the circuitry/logic performs, as described herein.

Moreover, while illustrated as a single component in FIG. 40 forpurposes of explanation, the traversal/intersection unit 4005 maycomprise a distinct traversal unit and a separate intersection unit,each of which may be implemented in circuitry and/or logic as describedherein.

In one embodiment, when a ray intersects a traversal node during aninner traversal, a traversal shader is spawned. The sorting circuitry4008 groups these shaders by shader record pointers 4201A-B, n to createa SIMD batch which is launched by the scheduler 4007 for SIMD executionon the graphics SIMD cores/EUs 4001. Traversal shaders can modifytraversal in several ways, enabling a wide range of applications. Forexample, the traversal shader can select a BVH at a coarser level ofdetail (LOD) or transform the ray to enable rigid body transformations.The traversal shader then spawns inner traversal for the selected BVH.

Inner traversal computes ray-BVH intersections by traversing the BVH andcomputing ray-box and ray-triangle intersections. Inner traversal isspawned in the same manner as shaders by sending a message to themessaging circuitry 4004 which relays the corresponding spawn message tothe ray-BVH intersection circuitry 4005 which computes ray-BVHintersections.

In one embodiment, the stack for inner traversal is stored locally inthe fixed-function circuitry 4010 (e.g., within the L1 cache 4006). Whena ray intersects a leaf node corresponding to a traversal shader or anintersection shader, inner traversal is terminated and the inner stackis truncated. The truncated stack along with a pointer to the ray andBVH is written to memory at a location specified by the calling shaderand then the corresponding traversal shader or intersection shader isspawned. If the ray intersects any triangles during inner traversal, thecorresponding hit information is provided as input arguments to theseshaders as shown in the below code. These spawned shaders are grouped bythe sorting circuitry 4008 to create SIMD batches for execution.

struct HitInfo {

-   -   float barycentrics[2];    -   float tmax;    -   bool innerTravComplete;    -   uint primID;    -   uint geomID;    -   ShaderRecord*leafShaderRecord;

}

Truncating the inner traversal stack reduces the cost of spilling it tomemory. One embodiment of the invention uses the approach described inRestart Trail for Stackless BVH Traversal, High Performance Graphics(2010), pp. 107-111, to truncate the stack to a small number of entriesat the top of the stack, a 42-bit restart trail and a 6-bit depth value.The restart trail indicates branches that have already been taken insidethe BVH and the depth value indicates the depth of traversalcorresponding to the last stack entry. This is sufficient information toresume inner traversal at a later time.

Inner traversal is complete when the inner stack is empty and there nomore BVH nodes to test. In this case an outer stack handler is spawnedthat pops the top of the outer stack and resumes traversal if the outerstack is not empty.

In one embodiment, outer traversal executes the main traversal statemachine and is implemented in program code executed by the shaderexecution circuitry 4000. It spawns an inner traversal query under thefollowing conditions: (1) when a new ray is spawned by a hit shader or aprimary shader; (2) when a traversal shader selects a BVH for traversal;and (3) when an outer stack handler resumes inner traversal for a BVH.

As illustrated in FIG. 44, before inner traversal is spawned, space isallocated on the call stack 4405 for the fixed-function circuitry 4010to store the truncated inner stack 4410. Offsets 4403-4404 to the top ofthe call stack and the inner stack are maintained in the traversal state4400 which is also stored in memory 2511. The traversal state 4400 alsoincludes the ray in world space 4401 and object space 4402 as well ashit information for the closest intersecting primitive.

The traversal shader, intersection shader and outer stack handler areall spawned by the ray-BVH intersection circuitry 4005. The traversalshader allocates on the call stack 4405 before initiating a new innertraversal for the second level BVH. The outer stack handler is a shaderthat is responsible for updating the hit information and resuming anypending inner traversal tasks. The outer stack handler is alsoresponsible for spawning hit or miss shaders when traversal is complete.Traversal is complete when there are no pending inner traversal queriesto spawn. When traversal is complete and an intersection is found, a hitshader is spawned; otherwise a miss shader is spawned.

While the hybrid traversal scheme described above uses a two-level BVHhierarchy, the embodiments of the invention described herein may use anarbitrary number of BVH levels with a corresponding change in the outertraversal implementation.

In addition, while fixed function circuitry 4010 is described forperforming ray-BVH intersections in the above embodiments, other systemcomponents may also be implemented in fixed function circuitry. Forexample, the outer stack handler described above may be an internal (notuser visible) shader that could potentially be implemented in the fixedfunction BVH traversal/intersection circuitry 4005. This implementationmay be used to reduce the number of dispatched shader stages and roundtrips between the fixed function intersection hardware 4005 and theprocessor.

The embodiments of the invention described here enable programmableshading and ray traversal control using user-defined functions that canexecute with greater SIMD efficiency on existing and future GPUprocessors. Programmable control of ray traversal enables severalimportant features such as procedural instancing, stochasticlevel-of-detail selection, custom primitive intersection and lazy BVHupdates.

Context-Aware Compression with Quantization of Hierarchical TransformMatrices for Instance Rendering

One embodiment of the invention reduces instance representations toachieve corresponding reductions in file sizes, memory utilization,power consumption, and runtime performance in both software and hardwareimplementations.

“Instancing” is an important tool in modelling complex scenes. At itsmost abstract, instancing refers to modelling a given object such as a atree or bush only once, and then re-using such a “base geometry”multiple times throughout a complex scene, such as when modelling anentire wooded and bushy landscape through several transformed “copies”of such base geometries. Large-scale scenes used in production renderingcan contain extraordinarily large numbers of instance transforms whichconsume significant amounts of memory, affect rendering performance, andmay be prohibitively large to fit into memory for accelerators withlocal memory such as GPUs.

These instances are often stored as 4×4 matrices made up of 16 32 bitfloating point values (“floats”) to store affine transforms describingthe translation, scale, and rotation of the given instance. In addition,many other transforms may be needed such as storing inverse transformsand multiple transforms with bounds for animated transforms. In therecently released Disney Moana Island scene, Matt Pharr reported thatthe renderer (Pbrt) initially used 16 GB of memory just for storingtransforms.

Mahovsky described an implementation for reducing memory consumption ofbounding volume hierarchies, BVHs, by using quantization of the treestructure through hierarchical reduced-precision integer representationsof the floating point bounds and achieved compression rates of over 60%.Note that instances are also often stored hierarchically through a scenegraph representation. For instance, a tree may have thousands of leaves,with each belonging to a branch, which belong to a trunk, which lieswithin the broader tree structure.

In one embodiment, instead of storing full 32 bit floating point numbersto represent each element of the transformation matrix, variable bitfloating point numbers or integers are utilized to represent offsets ofthe transformation from the parent transform. This embodiment reducesthe processing and storage requirements for instance representations toachieve corresponding reductions in file sizes, memory utilization,power consumption, and runtime performance in both software and hardwareimplementations.

FIG. 45 illustrates one embodiment of a GPU 3105 with an instanceprocessor 4504 to implement the techniques described herein. Theinstance processor 4504 may be implemented in fixed function circuitry,program code executed by the GPU 3105, or any combination thereof.Transform circuitry/logic 4510 of the instance processor 4504 performstransforms on base geometric objects 4501 to generate a plurality ofinstances 4501A-D.

Matrix compression circuitry/logic 4560 compresses a set of hierarchicaltransformation matrices 4505B to produce compressed transformationmatrices 4505A. In one embodiment, the matrix compressioncircuitry/logic 4560 implements the quantization techniques describedherein to generate the compressed transformation matrices 4505A. Forexample, 32-bit floating point data elements may be quantized to 16-bitor 8-bit variable floating point or integer values (as discussed ingreater detail below). As a result of the quantization, the quantizedtransformation matrices 4505A consume far less of the graphics memory3198 than the hierarchical transformation matrices 4505B.

In one embodiment, the matrix compression circuitry/logic 4560 reducesthe precision of certain elements of the hierarchical transformationmatrices 4505B in accordance with a required precision. A precisionanalysis unit 4540 determines an appropriate level of precision to beused for each transformation. The “appropriate” level may be based onerror tolerance, which is itself based on contextual parameters 4555such as distance from the camera and/or the type of ray being traversedby the traversal circuitry 6805. In one embodiment, the contextualparameters 4555 are determined at scene creation time, when the mainpoint of interest in the scene is known, or at runtime when the camerais moving based on camera distance. The precision analysis unit 4540uses one or more of these variables to determine a precision level. Thematrix compression unit 4560 uses this precision level to generate thequantized transformation matrices 4505A.

In one embodiment, the matrix compression circuitry/logic 4560 quantizesthe 32-bit floating point values of certain elements of the hierarchicaltransformation matrices 4505B to variable bit floating point numbers orintegers. In one implementation, the variable bit floating point valuesor integers generated for child transforms comprise offsets from theparent transforms. As described below, a subset of the hierarchicaltransformation matrices 4505B may be selected for quantization based onthe current traversal context 4555 (with those not selected beingignored or discarded).

In addition to quantizing 32 bit floating point values to variableprecision floats or integers, one embodiment of the invention exploitsalternate methods of data reduction. For example, one embodiment of thematrix compression circuitry/logic 4560 reduces the size of thequantized transformation matrices 4505A by storing quantized values foronly a subset of the hierarchical transformation matrices 4505B neededto adequately generate the instances 4501A-D. In addition, alternaterepresentations for the matrices.

In one embodiment illustrated in FIG. 46, a procedural transformgenerator 4661 procedurally generates the transform matrix 4605C inaccordance with the contextual parameters 4550 associated with thecurrent scene. The transform circuitry/logic 4510 then uses theprocedurally generated transform matrix 4605C to transform the basegeometry 4501.

While procedural transform generation is illustrated as a separateembodiment in FIG. 46, the procedural transform generation techniquesmay be included in the embodiment shown in FIG. 45 (e.g., matrixcompression circuitry/logic 4560 may include circuitry/logic forprocedural transform generation).

By way of example, and not limitation, only a 3×4 matrix may be storedin the quantized/procedurally generated transformation matrices 4505A,C, instead of 4×4 as in the hierarchical transformation matrices whenprojections are not needed. Only 3 floats may be used for many instancesthat need a basic translation in 3D space, and/or only 2 floats may beused when specifying a 2D translation onto a mesh face. Many otherconfigurations using different subsets of the full transform may beimplemented to reduce data storage requirements.

To produce the procedurally generated transform matrices 4505C, oneembodiment of the procedural transform generator 4561 evaluates the sizeand location of the instances 4501A-D. For example, a different set ofoperations are performed when many scenes with large instances aregenerated with the instances randomly placed at surface points (e.g.,such as clumps of grass on terrain). In this case, instead of explicitlystoring a full transform for the instance 4501A of the clump of grass,for example, only 2D offsets onto the terrain heightmap are needed andthe resulting translation can be computed during traversal.

FIG. 47 illustrates one particular implementation in which multiplelevels of compression are performed on the transform matrices 4505B. Aprecision conversion operation 4701 is performed to reduce the precisionof matrix elements from 32 bit floats to 8 bit integers. Thus, the 4×4matrix elements (as indicated by matrix notation m[16]) are convertedfrom float to 8-bit integer. A sub-setting operation 4702 is thenimplemented when it is noted that the transform is not time-varying andonly needs a 3×4 matrix. Thus, the m[16] matrix format is converted tom[12]. A further context-aware sub-setting 4703 is then implemented onthe m[12] matrix upon determining that a large number of childtransforms only apply rotations along the y axis (int8 Yrotation). Inthis particular example, a single 8 bit integer is used for the y axisrotation, which is stored in the compressed transform matrices 4505A.

In embodiments, the term “engine” or “module” or “logic” may refer to,be part of, or include an application specific integrated circuit(ASIC), an electronic circuit, a processor (shared, dedicated, orgroup), and/or memory (shared, dedicated, or group) that execute one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. In embodiments, an engine, module, or logic may beimplemented in firmware, hardware, software, or any combination offirmware, hardware, and software.

Embodiments of the invention may include various steps, which have beendescribed above. The steps may be embodied in machine-executableinstructions which may be used to cause a general-purpose orspecial-purpose processor to perform the steps. Alternatively, thesesteps may be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components.

As described herein, instructions may refer to specific configurationsof hardware such as application specific integrated circuits (ASICs)configured to perform certain operations or having a predeterminedfunctionality or software instructions stored in memory embodied in anon-transitory computer readable medium. Thus, the techniques shown inthe figures can be implemented using code and data stored and executedon one or more electronic devices (e.g., an end station, a networkelement, etc.). Such electronic devices store and communicate(internally and/or with other electronic devices over a network) codeand data using computer machine-readable media, such as non-transitorycomputer machine-readable storage media (e.g., magnetic disks; opticaldisks; random access memory; read only memory; flash memory devices;phase-change memory) and transitory computer machine-readablecommunication media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals, digitalsignals, etc.).

In addition, such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware. Throughout this detaileddescription, for the purposes of explanation, numerous specific detailswere set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, to one skilled in theart that the invention may be practiced without some of these specificdetails. In certain instances, well known structures and functions werenot described in elaborate detail in order to avoid obscuring thesubject matter of the present invention. Accordingly, the scope andspirit of the invention should be judged in terms of the claims whichfollow.

What is claimed is:
 1. An apparatus comprising: raytraversal/intersection circuitry to traverse rays through a hierarchicalacceleration data structure to identify intersections between rays andprimitives of a graphics scene; matrix compression circuitry/logic tocompress hierarchical transformation matrices to generate compressedhierarchical transformation matrices by quantizing N-bit floating pointdata elements associated with child transforms of the hierarchicaltransformation matrices to variable-bit floating point numbers orintegers comprising offsets from a parent transform of the childtransform, wherein the hierarchical transformation matrices storerepresentation of the hierarchical acceleration data structure; and aninstance processor to generate a plurality of instances of one or morebase geometric objects in accordance with the compressed hierarchicaltransformation matrices wherein instances of the one or more basegeometric objects form the primitives of the graphics scene.
 2. Theapparatus of claim 1 wherein the N-bit floating point data elementscomprise 32-bit floating point data elements and wherein thevariable-bit floating point numbers or integers comprise 8-bit dataelements.
 3. The apparatus of claim 1 wherein the instance processor isto analyze current context parameters associated with the ray traversalto determine a level of precision with which to quantize one or more ofthe N-bit floating point data elements.
 4. The apparatus of claim 3wherein the instance processor is further to determine the level ofprecision based on an error tolerance of the graphics scene.
 5. Theapparatus of claim 4 wherein the context parameters are to be evaluatedto determine the error tolerance, the error tolerance based on adistance from a camera to the primitives of the graphics scene and/or atype of ray being traversed by the ray traversal/intersection circuitry.6. The apparatus of claim 5 wherein the context parameters are evaluatedat creation time of the graphics scene and/or at runtime.
 7. Theapparatus of claim 6 wherein if the context parameters are evaluated atthe graphics scene creation time, the evaluation is associated with aparticular one or more objects in a region of interest and wherein ifthe context parameters are evaluated at the runtime, the evaluation isbased on the distance.
 8. The apparatus of claim 1 wherein the matrixcompression circuitry/logic to compress the hierarchical transformationmatrices comprises a procedural transform generator to procedurallygenerate the compressed hierarchical transformation matrices based onthe hierarchical transformation matrices.
 9. The apparatus of claim 1wherein the matrix compression circuitry/logic is to further compressthe hierarchical transformation matrices by selecting only a subset ofthe hierarchical transformation matrices and/or only a subset of dataelements of the hierarchical transformation matrices.
 10. The apparatusof claim 9 wherein the matrix compression circuitry/logic is todetermine the subset of the hierarchical transformation matrices and/orthe subset of data elements of the hierarchical transformation matricesbased on context parameters associated with ray traversal.
 11. A methodcomprising: traversing rays through a hierarchical acceleration datastructure to identify intersections between rays and primitives of agraphics scene; compressing hierarchical transformation matrices togenerate compressed hierarchical transformation matrices by quantizingN-bit floating point data elements associated with child transforms ofthe hierarchical transformation matrices to variable-bit floating pointnumbers or integers comprising offsets from a parent transform of thechild transform, wherein the hierarchical transformation matrices storerepresentation of the hierarchical acceleration data structure; andgenerating a plurality of instances of one or more base geometricobjects in accordance with the compressed hierarchical transformationmatrices, wherein instances of the one or more base geometric objectsform the primitives of the graphics scene.
 12. The method of claim 11wherein the N-bit floating point data elements comprise 32-bit floatingpoint data elements and wherein the variable-bit floating point numbersor integers comprise 8-bit data elements.
 13. The method of claim 11further comprising: analyzing current context parameters associated withtraversing the rays to determine a level of precision with which toquantize one or more of the N-bit floating point data elements.
 14. Themethod of claim 13 wherein the level of precision is to be determinedbased on an error tolerance of the graphics scene.
 15. The method ofclaim 14 wherein the context parameters are to be evaluated to determinethe error tolerance, the error tolerance based on a distance from acamera to the primitives of the graphics scene and/or a type of raybeing traversed.
 16. The method of claim 15 wherein the contextparameters are evaluated at creation time of the graphics scene and/orat runtime.
 17. The method of claim 16 wherein if the context parametersare evaluated at the graphics scene creation time, the evaluation isassociated with a particular one or more objects in a region of interestand wherein if the context parameters are evaluated at the runtime, theevaluation is based on the distance.
 18. The method of claim 11 whereincompressing the hierarchical transformation matrices comprisesprocedurally generating the compressed transformation matrices based onthe hierarchical transformation matrices.
 19. The method of claim 11further comprising: further compressing the hierarchical transformationmatrices by selecting only a subset of the hierarchical transformationmatrices and/or only a subset of data elements of the hierarchicaltransformation matrices.
 20. The method of claim 19 wherein the subsetof the hierarchical transformation matrices and/or the subset of dataelements of the hierarchical transformation matrices are determinedbased on context parameters associated with ray traversal.
 21. Anon-transitory machine-readable medium having program code storedthereon which, when executed by a machine, causes the machine to performthe operations of: traversing rays through a hierarchical accelerationdata structure to identify intersections between rays and primitives ofa graphics scene; compressing hierarchical transformation matrices togenerate compressed hierarchical transformation matrices by quantizingN-bit floating point data elements associated with child transforms ofthe hierarchical transformation matrices to variable-bit floating pointnumbers or integers comprising offsets from a parent transform of thechild transform, wherein the hierarchical transformation matrices storerepresentation of the hierarchical acceleration data structure; andgenerating a plurality of instances of one or more base geometricobjects in accordance with the compressed hierarchical transformationmatrices, wherein instances of the one or more base geometric objectsform the primitives of the graphics scene.
 22. The non-transitorymachine-readable medium of claim 21 wherein the N-bit floating pointdata elements comprise 32-bit floating point data elements and whereinthe variable-bit floating point numbers or integers comprise 8-bit dataelements.
 23. The non-transitory machine-readable medium of claim 21further comprising program code to cause the machine to perform theoperations of: analyzing current context parameters associated withtraversing the rays to determine a level of precision with which toquantize one or more of the N-bit floating point data elements.
 24. Thenon-transitory machine-readable medium of claim 23 wherein the level ofprecision is to be determined based on an error tolerance of thegraphics scene.
 25. The non-transitory machine-readable medium of claim24 wherein the context parameters are to be evaluated to determine theerror tolerance, the error tolerance based on a distance from a camerato the primitives of the graphics scene and/or a type of ray beingtraversed.
 26. The non-transitory machine-readable medium of claim 25wherein the context parameters are evaluated at creation time of thegraphics scene and/or at runtime.
 27. The non-transitorymachine-readable medium of claim 26 wherein if the context parametersare evaluated at the graphics scene creation time, the evaluation isassociated with a particular one or more objects in a region of interestand wherein if the context parameters are evaluated at the runtime, theevaluation is based on the distance.
 28. The non-transitorymachine-readable medium of claim 21 wherein compressing the hierarchicaltransformation matrices comprises procedurally generating the compressedtransformation matrices based on the hierarchical transformationmatrices.
 29. The non-transitory machine-readable medium of claim 21further comprising program code to cause the machine to perform theoperations of: further compressing the hierarchical transformationmatrices by selecting only a subset of the hierarchical transformationmatrices and/or only a subset of data elements of the hierarchicaltransformation matrices.
 30. The non-transitory machine-readable mediumof claim 29 wherein the subset of the hierarchical transformationmatrices and/or the subset of data elements of the hierarchicaltransformation matrices are determined based on context parametersassociated with ray traversal.